Machine Learning

Question: 44 A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster. Which nodes should the Specialist launch on Spot Instances? A. Master node B. Any of the core nodes C. Any of the task nodes D. Both core and task nodes

Answer(s): A

Question: 12 A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size. What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data? A. 1 shards B. 10 shards C. 100 shards D. 1,000 shards

Answer(s): B

Question: 4 A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows. Which parameter tuning guidelines should the Specialist follow to avoid overfitting? A. Increase the max_depth parameter value. B. Lower the max_depth parameter value. C. Update the objective to binary:logistic. D. Lower the min_child_weight parameter value.

Answer(s): B

Question: 54 A data scientist is training a text classification model by using the Amazon SageMaker built-in BlazingText algorithm. There are 5 classes in the dataset, with 300 samples for category A, 292 samples for category B, 240 samples for category C, 258 samples for category D, and 310 samples for category E. The data scientist shuffles the data and splits off 10% for testing. After training the model, the data scientist generates confusion matrices for the training and test sets. What could the data scientist conclude form these results? A. Classes C and D are too similar. B. The dataset is too small for holdout cross-validation. C. The data distribution is skewed. D. The model is overfitting for classes B and E.

Answer(s): B

Question: 6 A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance. What type of machine learning model should be used? A. Classification month-to-month using supervised learning of the 200 categories based on claim contents. B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month. C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month. D. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

Answer(s): C

Question: 8 An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 × 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks. The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras. Which approach should a Machine Learning Specialist take to obtain accurate predictions? A. Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes. B. Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object-detection single-shot multibox detector (SSD) al

Answer(s): C

Question: 95 A company that runs an online library is implementing a chatbot using Amazon Lex to provide book recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are only three categories implemented as the custom slot types: "comedy," "adventure," and "documentary." A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun," and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data in DynamoDB. How should the ML specialist fix the problem? A. Add the unrecognized words in the enumeration values list as new values in the slot type. B. Create a new custom slot type, add the unrecognized words to this slot type as enumeration values, and use this slot type for the slot. C. Use the AMAZON.SearchQuery built-in slot types for custom searches in the database. D. Add the unrecognized words as synonyms in the custom slot type.

Answer(s): C

Question: 15 A logistics company needs a forecast model to predict next month's inventory requirements for a single item in 10 warehouses. A machine learning specialist uses Amazon Forecast to develop a forecast model from 3 years of monthly data. There is no missing data. The specialist selects the DeepAR+ algorithm to train a predictor. The predictor means absolute percentage error (MAPE) is much larger than the MAPE produced by the current human forecasters. Which changes to the CreatePredictor API call could improve the MAPE? (Choose two.) A. Set PerformAutoML to true. B. Set ForecastHorizon to 4. C. Set ForecastFrequency to W for weekly. D. Set PerformHPO to true. E. Set FeaturizationMethodName to filling.

Answer(s): C,D Reference: https://docs.aws.amazon.com/forecast/latest/dg/forecast.dg.pdf

Question: 55 A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price. Which techniques should the company use for feature selection? (Choose three.) A. Data scaling with standardization and normalization B. Correlation plot with heat maps C. Data binning D. Univariate selection E. Feature importance with a tree-based classifier F. Data augmentation

Answer(s): C,D,F Reference: https://towardsdatascience.com/an-overview-of-data-preprocessing-features-enrichment-automatic-feature-selection-60b0c12d75ad https://towardsdatascience.com/feature-selection-using-python-for-classification-problem-b5f00a1c7028#:~:text=Univariate%20feature%20selection%20works%20by,analysis%20of%20variance%20(ANOVA).&text=That%20is%20why%20it%20is%20called%20'univariate' https://arxiv.org/abs/210A.04530

Question: 33 A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm- specific parameters to Amazon SageMaker. What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.) A. AWS Secrets Manager B. AWS CodeStar C. Amazon ECR D. Amazon ECS E. Amazon S3

Answer(s): C,E

Question: 11 The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data. Which machine learning algorithm should the researchers use that BEST meets their requirements? A. Latent Dirichlet Allocation (LDA) B. Recurrent neural network (RNN) C. K-means D. Convolutional neural network (CNN)

Answer(s): D

Question: 23 A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a convolutional neural network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container. Which action will provide the MOST secure protection? A. Remove Amazon S3 access permissions from the SageMaker execution role. B. Encrypt the weights of the CNN model. C. Encrypt the training and validation dataset. D. Enable network isolation for training jobs.

Answer(s): D

Question: 22 A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible. Which metrics should the data scientist use to optimize the model? (Choose two.) A. Specificity B. False positive rate C. Accuracy D. Area under the precision-recall curve E. True positive rate

Answer(s): D,E

Question: 13 A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training. How should a data scientist adjust the solution? A. Use the event tracker in Amazon Personalize to include real-time user interactions. B. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize. C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker. D. Add event type and event value fields to the interactions dataset in Amazon Personalize.

Answer(s): A

Question: 26 A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise. Which is the FASTEST route to index the assets? A. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes. B. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage. C. Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes. D. Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.

Answer(s): A

Question: 3 A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic. What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model? A. Exponential transformation B. Logarithmic transformation C. Polynomial transformation D. Sinusoidal transformation

Answer(s): A

Question: 49 A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of 1,000 hand-labeled images covering 10 distinct items. The training results were poor. Which machine learning approach fulfills the company's long-term needs? A. Convert the images to grayscale and retrain the model B. Reduce the number of distinct items from 10 to 2, build the model, and iterate C. Attach different colored labels to each item, take the images again, and build the model D. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.

Answer(s): A

Question: 51 A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing. The Data Scientist has been given the following requirements for the cloud solution: -Combine multiple data sources -Reuse existing PySpark logic -Run the solution on the existing schedule -Minimize the number of servers that will need to be managed Which architecture should the Data Scientist use to build this solution? A. Write the raw data to Amazon S3 Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule Use the existing PySpark logic to run the ETL job on the EMR cluster Output the results to a "processed" location m Amazon S3 that is accessible tor downstream use. B. Write the raw data to Amazon S3 Create an AWS Glue ETL job to perform the ETL processing against the input data Write the ETL job in PySpark to leverage the existing logic Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule Configure the output ta

Answer(s): A

Question: 71 A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents. The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy. Which process will improve the testing accuracy the MOST? A. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data. B. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score. C. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularizati

Answer(s): A

Question: 81 A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management. Which combination of AWS services will meet these requirements? A. Amazon EMR for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights B. Amazon Kinesis Data Analytics for data ingestion Amazon EMR for data discovery, enrichment, and transformation Amazon Redshift for querying and analyzing the results in Amazon S3 C. AWS Glue for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights D. AWS Data Pipeline for data transfer AWS Step Functions for orchestratin

Answer(s): A

Question: 86 A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations. Which solution should a machine learning specialist implement to meet these requirements? A. Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long. B. Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SN

Answer(s): A

Question: 98 A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds. The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket. What services should the data science team use to deliver this solution? A. Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends. B. Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of soc

Answer(s): A

Question: 68 A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required. How should the data scientist meet these requirements? A. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source. B. Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source. C. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source. D. Run a SageMaker training job with an EFS file system as the data source.

Answer(s): A Reference: https://aws.amazon.com/about-aws/whats-new/2019/08/amazon-sagemaker-works-with-amazon-fsx-lustre-amazon-efs-model-training/

Question: 87 A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible. How can the ML team solve this issue? A. Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances. B. Replace the current endpoint with a multi-model endpoint using SageMaker. C. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint. D. Increase the cooldown period for the scale-out activity.

Answer(s): A Reference: https://aws.amazon.com/blogs/machine-learning/configuring-autoscaling-inference-endpoints-in-amazon-sagemaker/

Question: 16 A data scientist wants to use Amazon Forecast to build a forecasting model for inventory demand for a retail company. The company has provided a dataset of historic inventory demand for its products as a .csv file stored in an Amazon S3 bucket. The table below shows a sample of the dataset. How should the data scientist transform the data? A. Use ETL jobs in AWS Glue to separate the dataset into a target time series dataset and an item metadata dataset. Upload both datasets as .csv files to Amazon S3. B. Use a Jupyter notebook in Amazon SageMaker to separate the dataset into a related time series dataset and an item metadata dataset. Upload both datasets as tables in Amazon Aurora. C. Use AWS Batch jobs to separate the dataset into a target time series dataset, a related time series dataset, and an item metadata dataset. Upload them directly to Forecast from a local machine. D. Use a Jupyter notebook in Amazon SageMaker to transform the data into the optimized protobuf recordIO format. Upload the dataset in this format to Amazon S3.

Answer(s): A Reference: https://docs.aws.amazon.com/forecast/latest/dg/dataset-import-guidelines-troubleshooting.html

Question: 57 A company wants to use automatic speech recognition (ASR) to transcribe messages that are less than 60 seconds long from a voicemail-style application. The company requires the correct identification of 200 unique product names, some of which have unique spellings or pronunciations. The company has 4,000 words of Amazon SageMaker Ground Truth voicemail transcripts it can use to customize the chosen ASR model. The company needs to ensure that everyone can update their customizations multiple times each hour. Which approach will maximize transcription accuracy during the development phase? A. Use a voice-driven Amazon Lex bot to perform the ASR customization. Create customer slots within the bot that specifically identify each of the required product names. Use the Amazon Lex synonym mechanism to provide additional variations of each product name as mis-transcriptions are identified in development. B. Use Amazon Transcribe to perform the ASR customization. Analyze the word confidence scores in the transcript, and automatically create or update a custom vocabulary file with any word that has a confidence score below an acceptable threshold value. Use this updated custom vocab

Answer(s): A Reference: https://docs.aws.amazon.com/lex/latest/dg/lex-dg.pdf

Question: 75 A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 ТВ of training data that consists of labeled images of defective product parts. The training data is in the corporate on-premises data center. The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments. Which solution will meet these requirements? A. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint. B. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint. C. Move the training data to an Amazo

Answer(s): A Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html

Question: 42 A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce. Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.) A. Cross-validation B. Numerical value binning C. High-degree polynomial transformation D. Logarithmic transformation E. One hot encoding

Answer(s): A,B

Question: 78 A company supplies wholesale clothing to thousands of retail stores. A data scientist must create a model that predicts the daily sales volume for each item for each store. The data scientist discovers that more than half of the stores have been in business for less than 6 months. Sales data is highly consistent from week to week. Daily data from the database has been aggregated weekly, and weeks with no sales are omitted from the current dataset. Five years (100 MB) of sales data is available in Amazon S3. Which factors will adversely impact the performance of the forecast model to be developed, and which actions should the data scientist take to mitigate them? (Choose two.) A. Detecting seasonality for the majority of stores will be an issue. Request categorical data to relate new stores with similar stores that have more historical data. B. The sales data does not have enough variance. Request external sales data from other industries to improve the model's ability to generalize. C. Sales data is aggregated by week. Request daily sales data from the source database to enable building a daily model. D. The sales data is missing zero entries for item sales

Answer(s): A,B Reference: https://towardsdatascience.com/sales-forecasting-from-time-series-to-deep-learning-5d115514bfac https://arxiv.org/ftp/arxiv/papers/1302/130B.6613.pdf

Question: 72 A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them. Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.) A. Amazon SageMaker seq2seq algorithm B. Amazon SageMaker BlazingText algorithm in Skip-gram mode C. Amazon SageMaker Object2Vec algorithm D. Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)

Answer(s): A,C Reference: https://aws.amazon.com/blogs/machine-learning/create-a-word-pronunciation-sequence-to-sequence-model-using-amazon-sagemaker/ https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec.html

Question: 24 A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet. Which combination of steps should the ML specialist take to secure the traffic? (Choose two.) A. Add a VPC endpoint policy to allow access to the IAM users. B. Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only. C. Modify the security group on the endpoint network interface to restrict access to the instances. D. Modify the ACL on the endpoint network interface to restrict access to the instances. E. Add a SageMaker Runtime VPC endpoint interface to the VPC.

Answer(s): A,C Reference: https://aws.amazon.com/blogs/machine-learning/private-package-installation-in-amazon-sagemaker-running-in-internet-free-mode/

Question: 59 A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables: The specialist chose a model that needs numerical input data. Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.) A. Apply integer transformation and set Red = 1, White = 5, and Green = 10. B. Add new columns that store one-hot representation of colors. C. Replace the color name string by its length. D. Create three columns to encode the color in RGB format. E. Replace each color name by its training set frequency.

Answer(s): A,D

Question: 46 A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set. What changes should the Specialist consider to solve this issue? (Choose three.) A. Choose a higher number of layers B. Choose a lower number of layers C. Choose a smaller learning rate D. Enable dropout E. Include all the images from the test set in the training set F. Enable early stopping

Answer(s): A,D,E

Question: 1 A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large? A. Use an Amazon SageMaker notebook for both feature engineering and model development B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development D. Use Amazon ML for both feature engineering and model development.

Answer(s): B

Question: 100 A data science team is planning to build a natural language processing (NLP) application. The application's text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet. Which solution can the team build MOST quickly to meet these requirements? A. Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks. B. Use an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier. C. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier. D. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

Answer(s): B

Question: 27 A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population. Which cross-validation strategy should the Data Scientist adopt? A. A k-fold cross-validation strategy with k=5 B. A stratified k-fold cross-validation strategy with k=5 C. A k-fold cross-validation strategy with k=5 and 3 repeats D. An 80/20 stratified split between training and validation

Answer(s): B

Question: 32 An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis. What approach would be the MOST effective to perform near-real time defect detection? A. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies. B. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML k-means clustering to determine anomalies. C. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies. D. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.

Answer(s): B

Question: 37 A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues. The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset. Which feature engineering technique should the Data Scientist use to meet the objectives? A. Run self-correlation on all features and remove highly correlated features B. Normalize all numerical values to be between 0 and 1 C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features D. Cluster raw data using k-means and use sample data from each cluster to build a new dataset

Answer(s): B

Question: 40 A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement? A. Logistic regression B. Linear regression C. K-means D. Principal component analysis (PCA)

Answer(s): B

Question: 41 A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker. How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly? A. Modify the bash_profile file in the container and add a bash command to start the training program B. Use CMD config in the Dockerfile to add the training program as a CMD of the image C. Configure the training program as an ENTRYPOINT named train D. Copy the training program to directory /opt/ml/train

Answer(s): B

Question: 43 A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible. What approach should the Specialist take to accomplish these tasks? A. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot. B. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot. C. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph. D. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.

Answer(s): B

Question: 48 A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset. The company requires at least 85% accuracy to make use of the model. After an exhaustive grid search, the optimal hyperparameters produced the following: -68% accuracy on the training set -67% accuracy on the validation set What can the machine learning specialist do to improve the system's accuracy? A. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters. B. Add more data to the training set and retrain the model using transfer learning to reduce the bias. C. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance. D. Train a new model using the current neural network architecture.

Answer(s): B

Question: 58 A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance. The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget. Which solution will result in the MOST cost savings? A. Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance. B. Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing. C. Change t

Answer(s): B

Question: 85 A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members' faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3. The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service. How should a machine learning specialist architect the solution to satisfy these requirements? A. Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support. B. Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation. C. Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN conne

Answer(s): B

Question: 90 A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations. What should the ML specialist do to resolve the violations? A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample. B. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline. C. Delete the endpoint and recreate it with the original configuration. D. Retrain the model again by using a combination of the original training set and the new training set.

Answer(s): B

Question: 94 A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has A.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result. A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days. What is the MOST direct approach to solve this problem within 2 days? A. Train a custom classifier by using Amazon Comprehend. B. Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet. C. Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker. D. Use a built-in seq2seq model in Amazon SageMaker.

Answer(s): B

Question: 9 A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings. To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities. Which deployment architecture for the model will address these business requirements? A. Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance. B. Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance. C. Deploy the model to an Amazon SageMaker batch transformation

Answer(s): B Reference: https://aws.amazon.com/blogs/iot/industrial-iot-from-condition-based-monitoring-to-predictive-quality-to-digitize-your-factory-with-aws-iot-services/ https://aws.amazon.com/blogs/iot/using-aws-iot-for-predictive-maintenance/

Question: 53 A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist. How should the data scientist split the dataset into a training and test set for this use case? A. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set. B. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set. C. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set. D. Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer(s): B Reference: https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagemaker/

Question: 17 A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU. Which architecture changes would ensure that provisioned resources are being utilized effectively? A. Redeploy the model as a batch transform job on an M5 instance. B. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance. C. Redeploy the model on a P3dn instance. D. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.

Answer(s): B Reference: https://aws.amazon.com/machine-learning/elastic-inference/

Question: 19 A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion. Which strategy will allow the data scientist to identify fraudulent accounts? A. Execute the built-in FindDuplicates Amazon Athena query. B. Create a FindMatches machine learning transform in AWS Glue. C. Create an AWS Glue crawler to infer duplicate accounts in the source data. D. Search for duplicate accounts in the AWS Glue Data Catalog.

Answer(s): B Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html

Question: 76 A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media. The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance. The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers. Which approach will address all of these requirements with the LEAST development effort? A. Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3. B. Load the data into Amazon DynamoD Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3. C. Create an AWS Glue job. Convert the scrip

Answer(s): B Reference: https://docs.aws.amazon.com/lambda/latest/dg/with-s3-example.html

Question: 67 A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance. Which approach should the ML specialist use to determine the ideal data transformations for the model? A. Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job. B. Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job. C. Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job. D. Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

Answer(s): B Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html

Question: 79 A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish. What steps could be used to accomplish this task? (Choose two.) A. Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis. B. Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics. C. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. D. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content. E. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.

Answer(s): B Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/lda.html

Question: 63 A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential. A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains. Which labeling approach will help the company improve this model? A. Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model. B. Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.

Answer(s): B Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-management- public.html

Question: 10 A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords. Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead? A. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data. B. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data. C. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords. D. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

Answer(s): B Reference: https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/tf-horovod-inference-pipeline/train.py

Question: 56 A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays. The data scientists are using Amazon Forecast to generate the forecasts. Which algorithm in Forecast should the data scientists use to meet these requirements? A. Autoregressive Integrated Moving Average (AIRMA) B. Exponential Smoothing (ETS) C. Convolutional Neural Network - Quantile Regression (CNN-QR) D. Prophet

Answer(s): B Reference: https://jesit.springeropen.com/articles/10.1186/s43067-020-00021-8

Question: 7 A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users. The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models. Which solution satisfies these requirements with MINIMAL effort? A. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer. B. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configura

Answer(s): B Explanation: A/B testing with Amazon SageMaker is required in the Exam. In A/B testing, you test different variants of your models and compare how each variant performs. Amazon SageMaker enables you to test multiple models or model versions behind the `same endpoint` using `production variants`. Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model. To test multiple models by `distributing traffic` between them, specify the `percentage of the traffic` that gets routed to each model by specifying the `weight` for each `production variant` in the endpoint configuration. Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html#model-testing-target-variant

Question: 64 An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible. Which steps would improve the accuracy of the solution? (Choose three.) A. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy. B. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset. C. Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images. D. Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3. E. Use Amazon Rekognition Custom Labels to train a new model. F. Check whether there are class imbalances in

Answer(s): B,C,E Reference: https://docs.aws.amazon.com/rekognition/latest/dg/how-it-works-types.html https://towardsdatascience.com/image-processing-techniques-for-computer-vision-11f92f511e21 https://docs.aws.amazon.com/rekognition/latest/customlabels-dg/training-model.html

Question: 20 A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations. The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist needs to reduce the number of false negatives. Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.) A. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE). B. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights. C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data. D. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer(s): B,D

Question: 65 A data engineer at a bank is evaluating a new tabular dataset that includes customer data. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other. Which steps should the data engineer take to address this issue? (Choose two.) A. Use a linear-based algorithm to train the model. B. Apply principal component analysis (PCA). C. Remove a portion of highly correlated features from the dataset. D. Apply min-max feature scaling to the dataset. E. Apply one-hot encoding category-based variables.

Answer(s): B,D Reference: https://royalsocietypublishing.org/doi/10.1098/rsta.2015.0202 https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html

Question: 80 A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment. Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.) A. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. B. Use SCPs to restrict access to SageMaker. C. Disable root access on the SageMaker notebook instances. D. Enable network isolation for training jobs and models. E. Restrict notebook presigned URLs to specific IPs used by the company. F. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer(s): B,D,E Reference: https://aws.amazon.com/blogs/machine-learning/millennium-management-secure-machine-learning-using-amazon-sagemaker/

Question: 66 A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset. Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.) A. Emails exchanged by customers and the company's customer service agents B. Social media posts containing the name of the company or its products C. A publicly available collection of news articles D. A publicly available collection of customer reviews E. Product sales revenue figures for the company F. Instruction manuals for the company's products

Answer(s): B,D,F

Question: 31 A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy. Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team's needs? (Choose two.) A. Add L1 regularization to the classifier B. Add features to the dataset C. Perform recursive feature elimination D. Perform t-distributed stochastic neighbor embedding (t-SNE) E. Perform linear discriminant analysis

Answer(s): B,E

Question: 28 A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interlaces. Which combination of services can be used to build this conversational interface? (Select THREE) A. Alexa for Business B. Amazon Connect C. Amazon Lex D. Amazon Poly E. Amazon Comprehend F. Amazon Transcribe

Answer(s): B,E,F

Question: 2 A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95. Which model describes the underlying data in this situation? A. A naive Bayesian model, since the features are all conditionally independent. B. A full Bayesian network, since the features are all conditionally independent. C. A naive Bayesian model, since some of the features are statistically dependent. D. A full Bayesian network, since some of the features are statistically dependent.

Answer(s): C

Question: 29 A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent. Why could this be an issue for the linear least squares regression model? A. It could cause the backpropagation algorithm to fail during training B. It could create a singular matrix during optimization which fails to define a unique solution C. It could modify the loss function during optimization causing it to fail during training D. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model

Answer(s): C

Question: 34 A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters. Which approach will provide the MAXIMUM performance boost? A. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector. B. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing. C. Reduce the learning rate and run the training process until the training loss stops decreasing. D. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.

Answer(s): C

Question: 36 A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users. Which storage option provides the most processing flexibility and will allow access control with IAM? A. Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users. B. Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies. C. Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies. D. Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

Answer(s): C

Question: 38 A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue. Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday. Which architecture should be used to scale the solution at the lowest cost? A. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance B. Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Schedu

Answer(s): C

Question: 39 A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible. Which techniques should be used to meet these requirements? A. Gather more data using Amazon Mechanical Turk and then retrain B. Train an anomaly detection model instead of an MLP C. Train an XGBoost model instead of an MLP D. Add class weights to the MLP's loss function and then retrain

Answer(s): C

Question: 50 A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent. How should the Specialist frame this business problem? A. Streaming classification B. Binary classification C. Multi-category classification D. Regression classification

Answer(s): C

Question: 52 A company is using Amazon Textract to extract textual data from thousands of scanned text-heavy legal documents daily. The company uses this information to process loan applications automatically. Some of the documents fail business validation and are returned to human reviewers, who investigate the errors. This activity increases the time to process the loan applications. What should the company do to reduce the processing time of loan applications? A. Configure Amazon Textract to route low-confidence predictions to Amazon SageMaker Ground Truth. Perform a manual review on those words before performing a business validation. B. Use an Amazon Textract synchronous operation instead of an asynchronous operation. C. Configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Perform a manual review on those words before performing a business validation. D. Use Amazon Rekognition's feature to detect text in an image to extract the data from scanned images. Use this information to process the loan applications.

Answer(s): C

Question: 74 A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration: During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.A. The best result had a learning rate of less than 0.0A. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue. Which solution provides the MOST accurate result? A. Modify the HPO configuration as follows: Select the most accurate hyperparameter configuration form this HPO job. B. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job: [0.01, 0.1] [0.001, 0.01] [0.0001, 0.001] Select the most accurate hyperparameter configuration form these three HPO jobs. C. Modify the HPO configuration as follows: Select the most accurate hyperparameter configuration for

Answer(s): C

Question: 77 A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest. Which next step is MOST likely to improve the data ingestion rate into Amazon S3? A. Increase the number of S3 prefixes for the delivery stream to write to. B. Decrease the retention period for the data stream. C. Increase the number of shards for the data stream. D. Add more consumers using the Kinesis Client Library (KCL).

Answer(s): C

Question: 89 A company offers an online shopping service to its customers. The company wants to enhance the site's security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested. The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user. Which approach should an ML specialist take to implement the new security feature in the web application? A. Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm. B. Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly. C. Use Amazon SageMaker Ground Truth to label each record as either a successfu

Answer(s): C

Question: 92 A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged. Which implementation will meet these requirements? A. Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3. B. Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes. C. Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3. D. Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.

Answer(s): C

Question: 93 A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead? A. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3. B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster. C. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3. D. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

Answer(s): C

Question: 97 A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket. How should the data scientist accomplish this? A. Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal. B. Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to. C. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket. D. Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

Answer(s): C

Question: 99 A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made. The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days. The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science. What should the data science team do to address this issue in the MOST operationally efficient manner? A. Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an e

Answer(s): C

Question: 82 A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers. The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset. Which solution for text extraction and entity detection will require the LEAST amount of effort? A. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities. B. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities. C. Extract text from

Answer(s): C Reference: https://aws.amazon.com/blogs/machine-learning/building-an-nlp-powered-search-index-with-amazon-textract-and-amazon-comprehend/

Question: 14 An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on- premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining. How should a machine learning specialist meet these requirements? A. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3. B. Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job. C. Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3. D. Use PostgreSQL logical repl

Answer(s): C Reference: https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Source.PostgreSQL.html

Question: 84 A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker. How can the data scientist meet this requirements? A. Call the CreateNotebookInstanceLifecycleConfig API operation B. Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance C. Stop and then restart the SageMaker notebook instance D. Call the UpdateNotebookInstanceLifecycleConfig API operation

Answer(s): C Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-software-updates.html

Question: 21 A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long. Which action will resolve the problem? A. Change preprocessing to use n-grams. B. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count. C. Adjust hyperparameters related to the attention mechanism. D. Choose a different weight initialization type.

Answer(s): C Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/seq-2-seq-howitworks.html

Question: 25 A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline. Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort? A. Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions. B. Create an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions. C. Create a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions. D. Create a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify. Build a labeling workflow backend us

Answer(s): C Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-private.html

Question: 69 A retail company uses a machine learning (ML) model for daily sales forecasting. The company's brand manager reports that the model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies. What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately? A. Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period. B. Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period. C. Create a line chart with the weekly mean absolute error (MAE) of the model. D. Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.

Answer(s): C Reference: https://machinelearningmastery.com/time-series-forecasting-performance-measures-with-python/

Question: 62 A company wants to create a data repository in the AWS Cloud for machine learning (ML) projects. The company wants to use AWS to perform complete ML lifecycles and wants to use Amazon S3 for the data storage. All of the company's data currently resides on premises and is 40 ТВ in size. The company wants a solution that can transfer and automatically update data between the on- premises object storage and Amazon S3. The solution must support encryption, scheduling, monitoring, and data integrity validation. Which solution meets these requirements? A. Use the S3 sync command to compare the source S3 bucket and the destination S3 bucket. Determine which source files do not exist in the destination S3 bucket and which source files were modified. B. Use AWS Transfer for FTPS to transfer the files from the on-premises storage to Amazon S3. C. Use AWS DataSync to make an initial copy of the entire dataset. Schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS. D. Use S3 Batch Operations to pull data periodically from the on-premises storage. Enable S3 Versioning on the S3 bucket to protect against accidental overwrites.

Answer(s): C Explanation: Configure DataSync to make an initial copy of your entire dataset, and schedule subsequent incremental transfers of changing data until the final cut-over from on-premises to AWS. Reference: https://aws.amazon.com/datasync/faqs/

Question: 35 A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only. What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally? A. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR. B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3. C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub. D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

Answer(s): D

Question: 45 A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices. Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity? A. Plot a histogram of the features and compute their standard deviation. Remove features with high variance. B. Plot a histogram of the features and compute their standard deviation. Remove features with low variance. C. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores. D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

Answer(s): D

Question: 47 A Machine Learning Specialist is attempting to build a linear regression model. Given the displayed residual plot only, what is the MOST likely problem with the model? A. Linear regression is inappropriate. The residuals do not have constant variance. B. Linear regression is inappropriate. The underlying data has outliers. C. Linear regression is appropriate. The residuals have a zero mean. D. Linear regression is appropriate. The residuals have constant variance.

Answer(s): D

Question: 5 A data scientist is developing a pipeline to ingest streaming web traffic data. The data scientist needs to implement a process to identify unusual web traffic patterns as part of the pipeline. The patterns will be used downstream for alerting and incident response. The data scientist has access to unlabeled historic data to use, if needed. The solution needs to do the following: -Calculate an anomaly score for each web traffic entry. -Adapt unusual event identification to changing web patterns over time. Which approach should the data scientist implement to meet these requirements? A. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker Random Cut Forest (RCF) built-in model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to perform data enrichment by calling the RCF model to calculate the anomaly score for each record. B. Use historic web traffic data to train an anomaly detection model using the Amazon SageMaker built-in XGBoost model. Use an Amazon Kinesis Data Stream to process the incoming web traffic data. Attach a preprocessing AWS Lambda function to per

Answer(s): D

Question: 70 An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location. Why does the XGBoost model NOT meet the current requirements, and how can this be fixed? A. The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment. B. The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment. C. The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment. D. The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.

Answer(s): D

Question: 73 A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category. Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category. The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise. Which solution meets these requirements? A. Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue. B. Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to t

Answer(s): D

Question: 83 A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow. Which set of actions should the ML specialist take to meet these requirements? A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook. B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon

Answer(s): D

Question: 88 A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences. Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time. How can the company implement the testing model with the LEAST amount of operational overhead? A. Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version. B. Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Applicati

Answer(s): D

Question: 96 A manufacturing company uses machine learning (ML) models to detect quality issues. The models use images that are taken of the company's product at the end of each production step. The company has thousands of machines at the production site that generate one image per second on average. The company ran a successful pilot with a single manufacturing machine. For the pilot, ML specialists used an industrial PC that ran AWS IoT Greengrass with a long-running AWS Lambda function that uploaded the images to Amazon S3. The uploaded images invoked a Lambda function that was written in Python to perform inference by using an Amazon SageMaker endpoint that ran a custom model. The inference results were forwarded back to a web service that was hosted at the production site to prevent faulty products from being shipped. The company scaled the solution out to all manufacturing machines by installing similarly configured industrial PCs on each production machine. However, latency for predictions increased beyond acceptable limits. Analysis shows that the internet connection is at its capacity limit. How can the company resolve this issue MOST cost-effectively? A. Set up a 10 Gb

Answer(s): D

Question: 91 A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates. Which solution will meet these requirements? A. Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates. B. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly. C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data. D. Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Answer(s): D Reference: https://aws.amazon.com/lake-formation/features/

Question: 30 A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS. How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3? A. Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance. B. Сonfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role. C. Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role. D. Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

Answer(s): D Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/encryption-at-rest.html

Question: 18 A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance. How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use? A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands. B. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance. C. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook. D. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.

Answer(s): D Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/nbi-add-external.html https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84

Question: 61 A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as "a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords. What should the data scientist do to meet these requirements? A. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket. B. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job. C. Use the SageMaker built-in Object Detection a

Answer(s): D Reference: https://towardsdatascience.com/natural-language-processing-count-vectorization-with-scikit-learn-e7804269bb5e

Question: 60 A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion. How will the data scientist MOST effectively model the problem? A. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem. B. The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem. C. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach. D. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Answer(s): D Reference: https://www.hindawi.com/journals/jat/2021/8878011/