Azure AI-900 Questions Dump

What are two automated document processing models Form Recognizer supports?

A pre-build receipt model, Custom model Form Recognizer service is one of the Azure Computer vision solutions additional to Computer Vision service, Custom Vision Service and Face service. For automated document processing, Form Recognizer uses two models: Custom Model and a pre-build receipt model. With the Custom Model approach, you train the Form Recognizer model based on your own form and data. You just need only 5 samples of your form to start. A pre-build receipt model is a Form Recognizer default model that is trained to work with receipts. It helps recognize receipts and extract data from them.

Which explanation best describes an indexer and an index?

An indexer converts documents into JSON and forwards them to a search engine for indexing.

What are the four types of Compute resources you can use in Azure Machine Learning Studio?

Attached compute, Compute Instances, Compute Clusters, Inference Clusters When you open Compute blade in Microsoft Azure Machine Learning Studio, you can see all four compute resources:

You plan to use a set of images to train an object detection model, and then publish the model as a predictive service. You want to use a single Azure resource with the same key and endpoint for training and prediction. What kind of Azure resource should you create?

Cognitive Services

You want to extract text from images and then use the Text Analytics service to analyze the text. You want developers to require only one key and endpoint to access all of your services. What kind of resource should you create in your Azure subscription?

Cognitive Services

You want to use the Computer Vision service to analyze images. You also want to use the Language service to analyze text. You want developers to require only one key and endpoint to access all of your services. What kind of resource should you create in your Azure subscription?

Cognitive Services

What tasks does Computer Vision Cognitive service include? Select all that apply.

Detects Objects, Read the text in the image, Categorize image, Identifies Landmarks Computer Vision service is one of the main areas of Artificial Intelligence. It belongs to the group of Azure Computer vision solutions such as Computer Vision service, Custom Vision Service, Face service, and Form Recognizer. Computer Vision service works with images. This service brings sense to the image pixels by using them as features for ML models. These predefined models help categorize and classify images, detect and recognize objects, tag, and identify them. Computer Vision can "read" a text in images in 25 languages and recognize landmarks. Natural Language Processing is incorrect because the Translator Text is a Natural Language Processing service and is not a Computer Vision service. Process Forms is incorrect because the Process Forms service is part of Azure Computer vision solutions and is not a Computer Vision service. Find Similar Faces because the Find Similar Faces is the technique that is part of Face service and is not a Computer Vision service.

You are working for a car dealership. Your boss asks you to provide him forecast information: will the new car model be successful or not. The new model has a variety of engine improvements, more comfortable seats, and a sunroof. You compiled the list of data about previous successful models with their characteristics and sales numbers. What should you do in the pre-processing data stage that would help you to predict the success of the new model?

Feature Selection During pre-processing, you need to work with data to select features that influence the label prediction. In this problem, features are the engine characteristics (power or volume), seat comforts, etc. They could help the ML model to predict the success of the new car model. Maybe the sunroof is not essential for predicting the label, and we need to discard this feature from the final set of features that we will use for model training. In short, Feature selection helps us to narrow down the features that are important for our label prediction and discard all features that don't play or play a minimal role in a label prediction. As a result, our trained model and prediction will be more efficient. All other options are incorrect because they are parts of the different data processing events that are irrelevant to the pre-processing (Training set selection or Data for model evaluation selection) or too generic (Data selection or Data Classification).

What are the four typical steps of data transformation for model training?

Impute missing values -> Normalize numeric values, Finding and removing data outliers, Feature selection After we ingest the data, we need to do a data preparation or transformation before supplying it for model training. There are four typical steps for data transformation such as Feature selection, Finding and removing data outliers, Impute missing values, and Normalize numeric features. Split data is coming after data transformation. ML algorithm selection data is coming after data transformation and Split Data steps.

A predictive app provides audio output for visually impaired users. Which principle of Responsible AI is reflected here?

Inclusiveness

You need to use the Translator service to translate email messages from Spanish into both English and French. What is the most efficient way to accomplish this goal?

Make a single call to the service; specifying a "from" language of "es", a "to" language of "en", and another "to" language of "fr".

You need to train and test your ML model. You prepare data for model training. Several of your numeric features have different scales. The first feature has a minimum value of 0.253 and a max of 0.987, the second one - from 12 to 124, and the last one - from 13545 to 56798. You need to bring them to a common scale. You decide to use feature engineering to address this problem. Does this decision help you to achieve your goal?

No You need to normalize your numeric features. The process of normalization brings numeric features to a common scale. Feature engineering is the method of creating new features based on the existing ones.

The customer service of your company spends a lot of time answering the same questions. They asked you to help them to automate this process. They provided you with a Microsoft Excel (*.xlsx) document with frequently asked questions and typical answers. What service will you use to create a knowledge base from this document?

QnA Maker You need to use the QnA Maker service. First, you need to provision the QnA Maker resource in your Azure subscription. After that, you can populate the newly created knowledge base using a frequently asked questions (FAQ) document. Azure Bot Service facilitates access to the knowledge base, but this service doesn't create a knowledge base. Custom vision service helps create your computer vision model, but this service doesn't create a knowledge base.  Text Analytics helps analyze text documents, detect documents' language, extract key phrases, determine entities, and provide sentiment analysis. This service doesn't create a knowledge base. Language Understanding Intelligent Service (LUIS) helps understand voice or text commands. This service doesn't create a knowledge base.

Ensuring an AI system does not provide a prediction when important fields contain unusual or missing values is .... principle for responsible AI

Reliability and safety

You need to create a new pipeline to train a regression model using Azure ML Designer. You ingest your data for the model and drop it on the canvas. What module would you typically drag-and-drop next on the canvas?

Select columns in the dataset After we bring data for model training or ingest data, the next stage is the Data transformation. Data transformation or data pre-processing usually includes the following steps: feature selection, data cleaning, and data normalization. In Azure ML Designer, we need to drag-and-drop the "Select Columns in Dataset" module from the Data Transformation section. Then on the right-side panel, we can select all the features we want to use for the model training.

You are developing an application that must take English input from a microphone and generate a real-time text-based transcription in Hindi. Which service should you use?

Speech

You use the Language service to perform sentiment analysis on a document, and a score of 0.99 is returned. What does this score indicate about the document sentiment?

The document is positive.

You created an AI solution. Along with solution deployment, you provided information about the solution's possibilities and limitations. By providing this information, with what principle for responsible AI did you comply?

Transparency Microsoft recognizes six principles of responsible AI: Fairness, Reliability and safety, Privacy and security, Transparency, Inclusiveness and Accountability. The principle of Transparency helps people to understand how to use AI solutions, their behavior, possibilities, and limitations.

You need to create a language model. What are the essential elements that you need to supply as data for your language model training?

Utterances, Entities, Intents For language model training, we need to provide the following key elements: Entities, Intents, and Utterance. We can achieve this by using the Azure Cognitive service LUIS portal. - Entity is the word or phrase that is the focus of the utterance, as the word "light" in the utterance "Turn the lights on." - Intent is the action or task that the user wants to execute. It reflects in utterance as a goal or purpose. We can define intent as "TurnOn" in the utterance "Turn the lights on." - Utterance is the user's input that your model needs to interpret, like "Turn the lights on" or "Turn on the lights".

For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.

Yes Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection No - Forecasting housing prices based on historical data is an example of anomaly detection - Predicting whether a patient will develop diabetes based on the patient's medical history is an example of anomaly detection Explanation: Anomaly detection encompasses many important tasks in machine learning:Identifying transactions that are potentially fraudulent.Learning patterns that indicate that a network intrusion has occurred.Finding abnormal clusters of patients.Checking values entered into a system.

You need to predict the animal population of an area.Which Azure Machine Learning type should you use? A. regression B. clustering C. classification

a Regression is a supervised machine learning technique used to predict numeric values.

Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point. A. Python B. R C. C# D. Scala

ab Explanation:Use Azure Machine Learning designer for customizing using Python and R code

A company employs a team of customer service agents to provide telephone and email support to customers.The company develops a webchat bot to provide automated answers to common customer queries.Which business benefit should the company expect as a result of creating the webchat bot solution? A. increased sales B. a reduced workload for the customer service agents C. improved product reliability

b

You build a machine learning model by using the automated machine learning user interface (UI).You need to ensure that the model meets the Microsoft transparency principle for responsible AI.What should you do? A. Set Validation type to Auto. B. Enable Explain best model. C. Set Primary metric to accuracy. D. Set Max concurrent iterations to 0.

b Model Explain Ability.Most businesses run on trust and being able to open the ML ג€black boxג€ helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.

For a machine learning progress, how should you split data for training and evaluation? A. Use features for training and labels for evaluation. B. Randomly split the data into rows for training and rows for evaluation. C. Use labels for training and features for evaluation. D. Randomly split the data into columns for training and columns for evaluation.

b The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling.

Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.Which type of AI workload should the company use? A. anomaly detection B. conversational AI C. computer vision D. natural language processing

c Azure's Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you're interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point. A. Calculate the accuracy of the model. B. Score test data by using the model. C. Combine multiple datasets. D. Use the model for real-time predictions. E. Remove records that have missing values.

ce

Select the answer that correctly completes the sentence. Counting the number of animals in an area based on a video feed is an example of

computer vision

You need to develop a mobile app for employees to scan and store their expenses while travelling.Which type of computer vision should you use? A. semantic segmentation B. image classification C. object detection D. optical character recognition (OCR)

d Azure's Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents - invoices, bills, financial reports, articles, and more.

To complete the sentence, select the appropriate option in the answer area.

Azure Kubernetes Service (AKS) To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint. Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.

DRAG DROP -Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.

Box 1: Regression -In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Box 2: Clustering -Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment. Box 3: Classification -Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.

DRAG DROP -You need to use Azure Machine Learning designer to build a model that will predict automobile prices. Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place:

Box 1: Select Columns in Dataset For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns. Box 2: Split data -Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed. Box 3: Linear regression -Because you want to predict price, which is a number, you can use a regression algorithm. For this example, you use a linear regression model.

To complete the sentence, select the appropriate option in the answer area. Hot Area:

Feature engineering

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

No, Yes, No Box 1: No -The validation dataset is different from the test dataset that is held back from the training of the model. Box 2: Yes -A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model's hyperparameters. Box 3: No -The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.

HOTSPOT -For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Yes, No, Yes, No Box 1: Yes -Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Box 2: No - Box 3: Yes -During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates throughML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment. Box 4: No -Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.The label is the column you want to predict.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Yes, Yes, No - Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset. - Regression is a machine learning task that is used to predict the value of the label from a set of related features.

HOTSPOT -For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Yes, Yes, No Box 1: Yes -Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models. Box 2: Yes -With the designer you can connect the modules to create a pipeline draft.As you edit a pipeline in the designer, your progress is saved as a pipeline draft. Box 3: No -

HOTSPOT -For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Yes, Yes, Yes

You are building an AI-based app.You need to ensure that the app uses the principles for responsible AI.Which two principles should you follow? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point. A. Implement an Agile software development methodology B. Implement a process of AI model validation as part of the software review process C. Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer D. Prevent the disclosure of the use of AI-based algorithms for automated decision making

bc

A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.This is an example of which type of machine learning? A. clustering B. regression C. classification

c

What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point. A. knowledgeability B. decisiveness C. inclusiveness D. fairness E. opinionatedness F. reliability and safety

cdf

DRAG DROP -You plan to deploy an Azure Machine Learning model as a service that will be used by client applications. Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order. Select and Place:

data preparation model training model evaluation

Select the answer that correctly completes the sentence.

fairness

To complete the sentence, select the appropriate option in the answer area.Hot Area:

fairness

You have the following dataset. You plan to use the dataset to train a model that will predict the house price categories of houses.What are Household Income and House Price Category? To answer, select the appropriate option in the answer area. NOTE: Each correct selection is worth one point.

feature, label

To complete the sentence, select the appropriate option in the answer

object detection

To complete the sentence, select the appropriate option in the answer area.

regression In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Incorrect Answers: ✑ Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data. ✑ Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.

To complete the sentence, select the appropriate option in the answer

regression Regression is a machine learning task that is used to predict the value of the label from a set of related features.

To complete the sentence, select the appropriate option in the answer area.

reliability and safety Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions.These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.

In Azure Machine Learning studio, what can you use to author regression machine learning pipelines using a drag-and-drop interface?

Designer

You plan to build an application that uses the Speech service to transcribe audio recordings of phone calls into text, and then submits the transcribed text to the Language service to extract key phrases. You want to manage access and billing for the application services in a single Azure resource. Which type of Azure resource should you create?

Cognitive Services

DRAG DROP -Match the types of AI workloads to the appropriate scenarios.To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.NOTE: Each correct selection is worth one point.

Computer vision NLP Anomaly detection Machine Learning (Regression)

What is one aspect that may impair facial detection?

Extreme angles

You created an AI solution that qualifies customers for a bank loan. The solution provides different results for the people living in cities and rural areas. What responsible AI principle does your solution violate?

Fairness The principle of Fairness directs AI solutions to treat everybody fairly, independently from gender, race, or any bias.

DRAG DROP -Match the principles of responsible AI to appropriate requirements. To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place:

Fairness Privacy and security Transparency

Ensuring that the numeric variables in training data are on a similar scale is an example of

Feature selection

To complete the sentence, select the appropriate option in the answer area.

Form Recognizer Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.

You plan to use the Form Recognizer pre-built receipt model. Which kind of Azure resource should you create?

Form Recognizer or Cognitive Services resource

What is the purpose of specifying granularity in your JSON data object?

It is used to indicate the recording pattern of the data.

Which data format is accepted by Azure Cognitive Search when you're pushing data to the index?

JSON

DRAG DROP -Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:

Knowledge mining Computer vision NLP Explanation: Box 1: Knowledge mining -You can use Azure Cognitive Search's knowledge mining results and populate your knowledge base of your chatbot. Box 2: Computer vision Box 3: Natural language processingNatural language processing (NLP) is used for tasks such as sentiment analysis.

DRAG DROP -Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:

Model evaluation Feature engineering Feature selection Box 1: Model evaluation -The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well asROC, Precision/Recall, and Lift curves. Box 2: Feature engineering -Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization. Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day. Box 3: Feature selection -In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.

You created a Custom Vision model. You want your model to detect trained objects on the photos.What information will you get about each object if you are using an object detection model?

Probability score, Bounding box, Class name Object detection is the form of ML that helps to recognize objects on the images. Each recognizable object will be put in the bounding box with the class name and probability score. Here is the Microsoft information about the object detection model:

An automobile dealership wants to use historic car sales data to train a machine learning model. The model should predict the price of a pre-owned car based on its make, model, engine size, and mileage. What kind of machine learning model should the dealership use automated machine learning to create?

Regression

DRAG DROP -Match the types of machine learning to the appropriate scenarios. To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:

Regression, clustering, classification Box 1: Regression -In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Box 2: Clustering -Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment. Box 3: Classification -Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.

DRAG DROP -Match the Microsoft guiding principles for responsible AI to the appropriate descriptions. To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.Select and Place:

Reliability and safety Accountability Privacy and security Box 1: Reliability and safety -To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Box 2: Accountability -The people who design and deploy AI systems must be accountable for how their systems operate. Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the final authority on any decision that impacts people's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems. Box 3: Privacy and security -As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used

You're using Azure Machine Learning designer to create a training pipeline for a binary classification model. You've added a dataset containing features and labels, a Two-Class Decision Forest module, and a Train Model module. You plan to use Score Model and Evaluate Model modules to test the trained model with a subset of the dataset that wasn't used for training. What's another module should you add?

Split Data

If you set up a search index without including a skillset, which would you still be able to query?

Text content

You want to use the Speech service to build an application that reads incoming email message subjects aloud. Which API should you use?

Text-to-Speech

You have published your Conversational Language Understanding application. What information does a client application developer need to get predictions from it?

The endpoint and key for the application's prediction resource

You use an Azure Machine Learning designer pipeline to train and test a binary classification model. You review the model's performance metrics in an Evaluate Model module, and note that it has an AUC score of 0.3. What can you conclude about the model?

The model performs worse than random guessing.

You have published an image classification model. What information must you provide to developers who want to use it?

The project ID, the model name, and the key and endpoint for the prediction resource

You are authoring a Conversational Language Understanding application to support an international clock. You want users to be able to ask for the current time in a specified city, for example "What is the time in London?".

What should you do? Define a "city" entity and a "GetTime" intent with utterances that indicate the city intent.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.

Yes - For regression model, labels must be numeric No - For clustering model, labels must be used - For classification model, labels must be numeric Explanation: Box 1: Yes -For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale. Box 2: No -K-Means Clustering -Because the K-means algorithm is an unsupervised learning method, a label column is optional.If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model.If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data. Box 3: No -For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.

Yes -Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI No - A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI - An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI Explanation: Box 1: Yes -Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Box 2: No -A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy. Box 3: No -Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.

HOTSPOT -For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Yes, No, No Box 1: Yes -In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict. In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing. Box 2: No - Box 3: No -Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn't really capture the effectiveness of a classifier.

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Yes, Yes, No Box 1: Yes -Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found. Box 2: Yes -The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Box 3: No -Custom Vision service can be used only on graphic files.

You are building a tool that will process images from retail stores and identify the products of competitors.The solution will use a custom model.Which Azure Cognitive Services service should you use? A. Custom Vision B. Form Recognizer C. Face D. Computer Vision

a

Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents? A. Form Recognizer B. Text Analytics C. Language Understanding D. Custom Vision

a Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/ value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.

When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI? A. transparency B. inclusiveness C. fairness D. privacy and security

a Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Incorrect Answers: B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments. C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual. D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy.

Which metric can you use to evaluate a classification model? A. true positive rate B. mean absolute error (MAE) C. coefficient of determination (R2) D. root mean squared error (RMSE)

a What does a good model look like? An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.

What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. coefficient of determination (R2) B. F1 score C. root mean squared error (RMSE) D. area under curve (AUC) E. balanced accuracy

ac A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative. C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled. Incorrect Answers: B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model. D: aucROC or area under the curve (AUC) is used to evaluate a classification model.

You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. Use a graphical user interface (GUI) to run automated machine learning experiments. B. Create a compute instance to use as a workstation. C. Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer. D. Create a dataset from a comma-separated value (CSV) file.

ac Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.

To complete the sentence, select the appropriate option in the answer area.

accuracy

In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point. A. Extract the invoice number from an invoice. B. Translate a form from French to English. C. Find image of product in a catalog. D. Identify the retailer from a receipt.

ad

Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A. dataset B. compute C. pipeline D. module

ad You can drag-and-drop datasets and modules onto the canvas.

Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point. A. dataset B. compute C. pipeline D. module

ad You can drag-and-drop datasets and modules onto the canvas.

To complete the sentence, select the appropriate option in the answer area.

adding and connecting modules on a visual canvas

You run a charity event that involves posting photos of people wearing sunglasses on Twitter. You need to ensure that you only retweet photos that meet the following requirements: ✑ Include one or more faces. ✑ Contain at least one person wearing sunglasses. What should you use to analyze the images? A. the Verify operation in the Face service B. the Detect operation in the Face service C. the Describe Image operation in the Computer Vision service D. the Analyze Image operation in the Computer Vision service

b

Which type of machine learning should you use to predict the number of gift cards that will be sold next month? A. classification B. regression C. clustering

b Explanation: In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

When training a model, why should you randomly split the rows into separate subsets? A. to train the model twice to attain better accuracy B. to train multiple models simultaneously to attain better performance C. to test the model by using data that was not used to train the model

c

You have a dataset that contains information about taxi journeys that occurred during a given period.You need to train a model to predict the fare of a taxi journey.What should you use as a feature? A. the number of taxi journeys in the dataset B. the trip distance of individual taxi journeys C. the fare of individual taxi journeys D. the trip ID of individual taxi journeys

b Explanation:The label is the column you want to predict. The identified Featuresare the inputs you give the model to predict the Label. Example: The provided data set contains the following columns: - vendor_id: The ID of the taxi vendor is a feature. - rate_code: The rate type of the taxi trip is a feature. - passenger_count: The number of passengers on the trip is a feature. - trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model. - trip_distance: The distance of the trip is a feature. - payment_type: The payment method (cash or credit card) is a feature. - fare_amount: The total taxi fare paid is the label.

You need to predict the sea level in meters for the next 10 years. Which type of machine learning should you use? A. classification B. regression C. clustering

b In the most basic sense, regression refers to prediction of a numeric target. Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

You have the Predicted vs. True chart shown in the following exhibit. Which type of model is the chart used to evaluate? A. classification B. regression C. clustering

b What is a Predicted vs. True chart? Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.

You are building an AI system. Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI? A. Ensure that all visuals have an associated text that can be read by a screen reader. B. Enable autoscaling to ensure that a service scales based on demand. C. Provide documentation to help developers debug code. D. Ensure that a training dataset is representative of the population.

c

Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups.This an example of which Microsoft guiding principle for responsible AI? A. accountability B. fairness C. inclusiveness D. privacy and security

c

You need to create a training dataset and validation dataset from an existing dataset.Which module in the Azure Machine Learning designer should you use? A. Select Columns in Dataset B. Add Rows C. Split Data D. Join Data

c A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets.The studio currently supports training/validation data splits

You need to create a training dataset and validation dataset from an existing dataset. Which module in the Azure Machine Learning designer should you use? A. Select Columns in Dataset B. Add Rows C. Split Data D. Join Data

c A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets. The studio currently supports training/validation data splits

Which type of machine learning should you use to identify groups of people who have similar purchasing habits? A. classification B. regression C. clustering

c Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset

You use Azure Machine Learning designer to publish an inference pipeline.Which two parameters should you use to access the web service? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. A. the model name B. the training endpoint C. the authentication key D. the REST endpoint

cd You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it. To consume the pipeline, you need: ✑ The REST endpoint for your service ✑ The Primary Key for your service

To complete the sentence, select the appropriate option in the answer area.

classification Two-class classification provides the answer to simple two-choice questions such as Yes/No or True/False.

To complete the sentence, select the appropriate option in the answer area.

features

To complete the sentence, select the appropriate option in the answer area.

reliability and safety Reliability and safety: AI systems need to be reliable and safe in order to be trusted. It is important for a system to perform as it was originally designed and for it to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation. Rigorous testing and validation should be established for operating conditions to ensure that the system responds safely to edge cases, and A/B testing and champion/challenger methods should be integrated into the evaluation process. An AI system's performance can degrade over time, so a robust monitoring and model tracking process needs to be established to reactively and proactively measure the model's performance and retrain it, as necessary, to modernize it.

Which of the following results does an object detection model typically return for an image?

A class label, probability, and bounding box for each object in the image

You plan to use Face to detect human faces in an image. How does the service indicate the location of the faces it detects?

A set of coordinates for each face, defining a rectangular bounding box around the face

You train an image classification model that achieves less than satisfactory evaluation metrics. How might you improve it?

Add more images to the training set.

You use Azure Machine Learning designer to create a training pipeline for a clustering model. Now you want to use the model in an inference pipeline. Which module should you use to infer cluster predictions from the model?

Assign Data to Clusters

You want to build a personal virtual assistant. What service will you use to connect your assistant with various input channels and devices?

Azure Bot Services

You want to create a model to predict sales of ice cream based on historic data that includes daily ice cream sales totals and weather measurements. Which Azure service should you use?

Azure Machine Learning

A bank wants to use historic loan repayment records to categorize loan applications as low-risk or high-risk based on characteristics like the loan amount, the income of the borrower, and the loan period. What kind of machine learning model should the bank use automated machine learning to create?

Classification

You need to deliver a support bot for internal use in your organization. Some users want to be able to submit questions to the bot using Microsoft Teams, others want to use a web chat interface on an internal web site. What should you do?

Create a knowledge base. Then create a bot for the knowledge base and connect the Web Chat and Microsoft Teams channels for your bot

You plan to use the Custom Vision service to train an image classification model. You want to create a resource that can only be used for model training, and not for prediction. Which kind of resource should you create in your Azure subscription?

Custom Vision

What is meant by seasonal data?

Data occurring at regular intervals.

Your organization has an existing frequently asked questions (FAQ) document. You need to create a knowledge base that includes the questions and answers from the FAQ with the least possible effort. What should you do?

Import the existing FAQ document into a new knowledge base.

You created a Personal Virtual Assistant. Select all responsible AI principles that your solution must follow.

Inclusiveness, Reliability and Safety, Privacy and Security Microsoft recognizes six principles of responsible AI: Fairness, Reliability and safety, Privacy and security, Transparency, Inclusiveness and Accountability. Therefore all other options are incorrect.

How does the Anomaly Detector service evaluate real-time data for anomalies?

It evaluates the current value against the previous value.

You want to use the Language service to determine the key talking points in a text document. Which feature of the service should you use?

Key phrase extraction

Assigning classes to images before training a classification model is an example of ...

Labeling

You need to train and test your model. You prepared data for model training. You decided to use all the data for model training and then for the model validation. Does this decision help you to achieve your goal?

No You have to split your data into two sets: the first is for model training and the second for model testing. If you are using Automated machine learning, it automatically does that for you as part of data preparation and model training.

You want to use the Computer Vision service to analyze images of locations and identify well-known buildings. What should you do?

Retrieve the categories for the image, specifying the landmarks domain

You plan to use the Computer Vision service to read text in a large PDF document. Which API should you use?

The Read API

When might you see NaN returned for a score in Language Detection?

When the language is ambiguous

You have a database that contains a list of employees and their photos.You are tagging new photos of the employees.For each of the following statements select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.

Yes - The Face service can be used to perform facial recognition for employees - The Face service will be more accurate if you provide more sample photos of each employee from different angles No - If an employee is wearing sunglasses, the Face service will always fail to recognize the employee

You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.This is an example of which Microsoft guiding principle for responsible AI? A. fairness B. inclusiveness C. reliability and safety D. accountability

b Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.

Your company created a new mobile phone. You need to define a price range (0 - low cost to 3 - very high cost) for the phone. You collected technical and sales data for the phones on the market. Now you are ready to train your model. Here is your train dataset: What type of model will you train?

Classification Model We are training the Classification model. In our case, we are using the historical data and predicting the price range category that a new phone belongs to. The "Price range" column is our target or label, and it has four classes: 0 (low cost), 1(medium cost), 2 (high cost), and 3 (very high cost). The model output value will be one of these four classes. The Regression model is wrong, the Regression model uses historical data for model training. But it predicts the output numeric value, not the class or classes. The Clustering model is wrong, the Clustering model clusters unlabeled data into groups based on some common properties. An unsupervised model uses unlabeled data.

You are using Text Analytics Sentiment API to analyze the following sentence: "Peter was surprised and very happy to meet Sara in Paris." What sentiment value should you expect in the API response?

Positive Sentiment Analysis is a Text Analytics service that helps analyze text and returns sentiment scores (between 0 and 1) for each sentence. A score close to 0 means a negative sentiment, and a score close to 1 means positive. In cases with a neutral or undefined sentiment, the score is 0.5. In our case, the sentiment of the sentence is positive, with a confidence score of 1.0.

Your company created a new mobile phone. You need to define a price range (0 - low cost to 3 - very high cost) for the phone. You collected technical and sales data for the phones on the market. Now you are ready to train your model. Here is your train dataset: What will be the label for this model?

Price Range The label is a generic name for the model output value or class. In our case, we are predicting the price range category that a new phone belongs to. The "Price range" column is our label. All other options are incorrect because these columns are inputs or features for the model.

You want to create a knowledge base for bots. What service would you use?

Question Answering

What is the ideal value of AUC?

1.0 Area Under Curve (AUC) is the model performance metrics for classification models. For binary classification models, the AUC value of 0.5 represents the random predictions. The model predictions are the same as randomly selected values of "Yes" or "No". If the AUC value is below 0.5, the model performance is worse than random. Ideally, the best-fitted model has a value of 1. Such an ideal model predicts all the values correctly.

You are developing a model to predict events by using classification.You have a confusion matrix for the model scored on test data as shown in the following exhibit. Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.NOTE: Each correct selection is worth one point.

11 1033 TP = True Positive. The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN). Box 2: 1,033 - FN = False Negative

You created a classification model with four possible classes. What will be the size of the confusion matrix?

4x4 The confusion matrix provides a tabulated view of predicted and actual values for each class. If we are predicting the classification for four classes, our confusion matrix will have 4x4 size.

You are using the Form Recognizer service to analyze receipts that you have scanned into JPG format images. What is the maximum file size of JPG file you can submit to the pre-built receipt model?

50 MB

You are building your AI solution within the framework of governance and organizational requirements that reflect defined legal and ethical standards. What responsible AI principle are you following?

Accountability Microsoft recognizes six principles of responsible AI: Fairness, Reliability and safety, Privacy and security, Transparency, Inclusiveness and Accountability. The principle of Accountability directs AI solutions to follow governance and organizational norms.

What is the name of the common AI service that provides 24/7 monitoring of the customer's time-series data for possible data irregularities?

Anomaly Detection Customers are using Anomaly detection APIs for constant monitoring of their time-series data. An anomaly detection service ingests the data and automatically selects the best ML model for the identification of the possible data irregularities. The service alerts the customers as soon as such anomalies arise. Anomaly Detection is one of the five key elements of Microsoft Artificial Intelligence. The other four are Machine Learning, Computer Vision, Natural Language Processing, and Conversational AI. Machine Learning, Computer Vision, Natural Language Processing, and Conversational AI, along with Anomaly detection, are the key elements of Artificial Intelligence. Automated Machine Learning is a feature of Machine Learning and is not part of Anomaly detection.

What metrics does Azure ML use for the Evaluation of the regression models?

Coefficient of Determination, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) Azure ML uses model evaluation for the measurement of the trained model accuracy. For regression models Evaluate Model module provides the following five metrics: Mean absolute error (MAE), Root mean squared error (RMSE), Relative absolute error (RAE), Relative squared error (RSE), and Coefficient of determination (R2). - Root Mean Squared Error (RMSE) is the regression model evaluation metrics. It represents the square Root from the squared mean of the errors between predicted and actual values. - Mean absolute error (MAE) is the regression model evaluation metrics. It produces the score that measures how close the model is to the actual values — the lower score, the better the model performance. - Coefficient of determination or R2 is the regression model evaluation metrics. It reflects the model performance: the closer R2 to 1 - the better the model fits the data. - Accuracy is the classification model evaluation metrics and is not the regression model evaluation metrics. - Number of Points is the clustering model evaluation metrics and is not the regression model evaluation metrics. - Combined Evaluation is the clustering model evaluation metrics and is not the regression model evaluation metrics. - Recall is the classification model evaluation metrics and is not the regression model evaluation metrics.

Your company created a new mobile phone. You need to define a price range (0 - low cost to 3 - very high cost) for the phone. You collected technical and sales data for the phones on the market. Now you are ready to train your model. Here is your train dataset: What column will you discard from the final dataset during feature selection?

Color Data pre-processing involves various techniques, like feature selection, normalization or feature engineering, etc. During feature selection, we identify features that would help us with label prediction. And we discard the rest. In our dataset, the Color feature wouldn't correlate with the label due to the constant value of "black." We can safely remove this feature from the final dataset. All other options should be included in the training dataset.

You are designing an AI application that uses images to detect cracks in car windshields and warn drivers when a windshield should be repaired or replaced. What AI workload is described?

Computer Vision

DRAG DROP -Match the types of AI workloads to the appropriate scenarios. To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.Select and Place:

Conversational AI Computer vision NLP Box 3: Natural language processing. Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.

You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service?

Create an inference pipeline from the training pipeline

What are the three main authoring tools on the Azure ML Studio home screen?

Designer, Automated ML, Notebooks

You install a Visual product search application on your mobile. The application searches products based on the images that you capture by mobile camera. What Computer Vision common task this application uses for the product search?

Image Classification You use your camera to capture a picture of the product. An application identifies this product utilizing the Image classification model and submits it for a search. The Image classification model helps to classify images based on their content. Object detection is incorrect because the Object detection model helps to identify objects and their boundaries within the image. Semantic segmentation is incorrect because the Semantic segmentation model helps classify pixels to the objects they belong to. Face detection is incorrect because Face detection is a Computer vision technique that helps detect and recognize people's faces. Image Analysis is incorrect because Image Analysis helps extract information from the images, tag them, and create a descriptive image summary.

You are working for a car dealership. Your boss asks you to provide him information about how many blue cars he needs to order for the next quarter. You decide to create an ML model and choose an unsupervised machine learning approach. Will this help you to achieve your goal?

No Your task is to provide a numeric prediction. You can achieve this by creating a regression model based on the historical sales data of the blue cars from previous quarters. Regression and Classification modeling types are two parts of Supervised machine learning. Only Clustering belongs to Unsupervised machine learning. If you choose the Unsupervised machine learning approach, you will not achieve your goal.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.

No - When creating an object detection model in the Custom Vision service, you must choose a classification type of either Multilabel or Multiclass Yes - You can create an object detection model in the Custom Vision service to find the location of content within an image - When creating an object detection model in the Custom Vision service, you can select from a set of predefined domains

For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.

No - You train a regression model by using unlabeled data - The classification technique is used to predict sequential numerical data over time Yes - Grouping items by their common characteristics is an example of clustering

You are creating a training pipeline for a regression model. You use a dataset that has multiple numeric columns in which the values are on different scales. You want to transform the numeric columns so that the values are all on a similar scale. You also want the transformation to scale relative to the minimum and maximum values in each column. Which module should you add to the pipeline?

Normalize Data

You want to use the Computer Vision service to identify the location of individual items in an image. Which of the following features should you retrieve?

Objects

You created a classification model. Below is the confusion matrix for this model: What is the name of the metric that uses TP/(TP+FN) expression for its value calculation?

Recall The confusion matrix (or error matrix) provides a tabulated view of predicted and actual values for each class. It is usually used as a performance assessment for classification models. A binary confusion matrix is divided into four squares that represent the following values: - True positive (TP) - the number of positive cases that the model predicted right. - True negative (TN) - the number of negative cases that the model predicted right. - False positive (FP) - the number of positive cases that the model falsely predicted right. - False negative (FN) - the number of negative cases that the model falsely predicted right. Recall metric defines how many positive cases that the model predicted are actually predicted right. We can calculate this metric using the following formula: TP/(TP+FN). Accuracy is incorrect: a formula for Accuracy metric calculation is (TP+TN)/Total number of cases. Precision is incorrect: a formula for Precision metric calculation is TP/(TP+FP). F1 Score is incorrect: a formula for F1 metric calculation is 2TP/(2TP+FP+FN). Selectivity is incorrect: this expression is for Selectivity (or true negative rate) metric calculation: TN/(TN+FP).

What are the three metrics that help for evaluate Custom vision model performance?

Recall, Average Precision (AP), Precision Custom vision is one of the Computer Vision tasks. Custom vision service helps create your own computer vision model. There are three main performance metrics for the Custom vision models: Precision, Recall, and Average Precision (AP). Precision defines the percentage of the class predictions that the model makes correct. For example, if the model predicts that ten images are bananas, and there are actually only seven bananas, the model precision is 70%. Recall defines the percentage of the class identification that the model makes correct. For example, if there are ten apple images, and the model identifies only eight, the model recall is 80%. Average Precision (AP) is the combined metrics of both Precision and Recall. Accuracy is a Classification model metric, but it is not used for Custom vision models performance assessments. Number of Points is a Clustering model metric and is not used for Custom vision models performance assessments. Mean Absolute Error (MAE) is a Regression model metric and is not used for Custom vision models performance assessments.

What is the name of the responsible AI principle that directs AI solutions design to include resistance to harmful manipulation?

Reliability and Safety. Microsoft recognizes six principles of responsible AI:Fairness, Reliability and safety, Privacy and security, Transparency, Inclusiveness and Accountability.The principle of Reliability and safety directs AI solutions to respond safely to non-standard situations and to resist harmful manipulations.

You implement an aerial image processing application to identify the flooded areas. What common Computer Vision task is this application using?

Semantic Segmentation When the application processes images, it uses Semantic segmentation to classify pixels that belong to the particular object (in our case, flooded areas) and highlights them. Object detection is incorrect because Object detection helps to identify objects and their boundaries within the image. Image classification is incorrect because Image classification helps to classify images based on their content. Face detection is incorrect because Face detection is a Computer vision technique that helps detect and recognize people's faces. Image Analysis is incorrect because Image Analysis helps extract information from the images, tag them, and create a descriptive image summary.

You are using an Azure Machine Learning designer pipeline to train and test a K-Means clustering model. You want your model to assign items to one of three clusters. Which configuration property of the K-Means Clustering module should you set to accomplish this?

Set Number of Centroids to 3

You want to use automated machine learning to train a regression model with the best possible R2 score. How should you configure the automated machine learning experiment?

Set the Primary metric to R2 score

Why do you split data into training and validation sets?

Splitting data into two sets enables you to compare the labels that the model predicts with the actual known labels in the original dataset.

You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table. For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point.

Yes Mass (kg) is a feature Quality Test is a label No Temperature (C) is a label