AWS Certified AI Practioner AIF-C01
Consider a scenario where a fully-managed AWS service needs to be used for automating the extraction of insights from legal briefs such as contracts and court records. What do you recommend? A) Amazon Translate B) Amazon Transcribe C) Amazon Comprehend D) Amazon Rekognition
C) Amazon Comprehend
A company uses a generative model to analyze animal images in the training dataset to record variables like different ear shapes, eye shapes, tail features, and skin patterns. Which of the following tasks can the generative model perform? A) The model can recreate new animal images that were not in the training dataset B) The model can classify a single species of animals such as cats C) The model can identify any image from the training dataset D) The model can classify multiple species of animals such as cats, dogs, etc
A) The model can recreate new animal images that were not in the training dataset
Which of the following summarizes the capabilities of a multimodal model? A) A multimodal model can accept a mix of input types such as audio/text and create a mix of output types such as video/image B) A multimodal model can accept only a single type of input, however, it can create a mix of output types such as video/image C) A multimodal model can accept a mix of input types such as audio/text, however, it can only create a single type of output D) A multimodal model can accept only a single type of input and it can only create a single type of output
A) A multimodal model can accept a mix of input types such as audio/text and create a mix of output types such as video/image
Which Integrated Development Environments (IDEs) are supported by Amazon SageMaker Studio for ML development? A) All B) JupyterLab C) Code Editor D) RStudio
A) All
Which AWS services/tools can be used to implement Responsible AI practices? (Select two) A) Amazon SageMaker Model Monitor B) Amazon SageMaker Clarify C) Amazon SageMaker JumpStart D) AWS Audit Manager E) Amazon Inspector
A) Amazon SageMaker Model Monitor E) Amazon Inspector
Which of the following are the advantages of cloud computing? (Select three) A) Benefit from massive economies of scale B) Spend money on building and maintaining data centers C) Go global in minutes and deploy applications in multiple regions around the world with just a few clicks D) Allocate a few months of planning for your infrastructure capacity needs E) Trade variable expense for capital expense F) Trade capital expense for variable expense
A) Benefit from massive economies of scale C) Go global in minutes and deploy applications in multiple regions around the world with just a few clicks F) Trade capital expense for variable expense
What is cloud computing as defined by AWS? A) Cloud computing refers to the on-demand delivery of IT resources and applications via the internet with pay-as-you-go pricing B) Cloud computing is the practice of using only open-source software for all computing needs C) Cloud computing is the process of using a single local server to store and process data D) Cloud computing involves manually managing physical data centers and networking hardware for data storage and processing
A) Cloud computing refers to the on-demand delivery of IT resources and applications via the internet with pay-as-you-go pricing
What is the difference between computer vision and image processing? A) Image processing focuses on enhancing and manipulating images for visual quality, whereas computer vision involves interpreting and understanding the content of images to make decisions B) Computer vision focuses on enhancing and manipulating images for visual quality, whereas image processing involves interpreting and understanding the content of images to make decisions C) Computer vision and image processing are identical fields with no distinct differences in their applications or techniques D) Image processing uses machine learning algorithms, while computer vision relies solely on pre-programmed rules See all questionsBackSkip question
A) Image processing focuses on enhancing and manipulating images for visual quality, whereas computer vision involves interpreting and understanding the content of images to make decisions
How does model training work in Deep Learning? A) Model training in deep learning involves using large datasets to adjust the weights and biases of a neural network through multiple iterations, using techniques such as gradient descent to minimize the error B) Model training in deep learning requires no data; the neural network automatically learns from predefined algorithms without any input C) Model training in deep learning involves manually setting the weights and biases of a neural network based on predefined rules D) Model training in deep learning involves only the use of support vector machines and decision trees to create predictive models
A) Model training in deep learning involves using large datasets to adjust the weights and biases of a neural network through multiple iterations, using techniques such as gradient descent to minimize the error
Which of the following represents the best-fit use cases for utilizing Retrieval augmented generation (RAG) in Amazon Bedrock? (Select two) A) Product recommendations that match shopper preferences B) Image generation from text prompt C) Customer service chatbot D) Original content creation E) Medical queries chatbot
A) Product recommendations that match shopper preferences C) Customer service chatbot
Which deployment model of Amazon SageMaker is the right fit for persistent and real-time endpoints that make one prediction at a time ? A) Real-time hosting services B) Asynchronous Inference C) Batch transform D) Serverless Inference
A) Real-time hosting services
Which of the following techniques is used by Foundation models to create labels from input data? A) Self-supervised learning B) Reinforcement learning C) Supervised learning D) Unsupervised learning
A) Self-supervised learning
Which of the following represents the capabilities of Amazon Q Developer? (Select two) A) Visualize your AWS account-specific cost-related data in Amazon Q Developer B) Modify your AWS resources to achieve cost-optimization C) Understand and manage your cloud infrastructure on AWS D) Deploy your cloud infrastructure on AWS E) Get answers to your AWS account-specific cost-related questions using natural language
A) Visualize your AWS account-specific cost-related data in Amazon Q Developer E) Get answers to your AWS account-specific cost-related questions using natural language
What is the primary difference between Amazon Mechanical Turk and Amazon Ground Truth? A) Amazon Mechanical Turk is used for creating labeled datasets using automated processes, whereas Amazon Ground Truth is a marketplace for outsourcing various tasks to a distributed workforce B) Amazon Mechanical Turk provides a marketplace for outsourcing various tasks to a distributed workforce, while Amazon Ground Truth is specifically designed for creating labeled datasets for machine learning, incorporating both automated and human labeling C) Amazon Mechanical Turk is exclusively for data labeling tasks, whereas Amazon Ground Truth supports a wide range of tasks including surveys and content moderation D) Amazon Mechanical Turk and Amazon Ground Truth are the same service, used interchangeably for any task involving human intelligence
B) Amazon Mechanical Turk provides a marketplace for outsourcing various tasks to a distributed workforce, while Amazon Ground Truth is specifically designed for creating labeled datasets for machine learning, incorporating both automated and human labeling
Which AWS service offers flexibility to include human input across the Machine Learning lifecycle to improve the accuracy and relevancy of the models? A) Amazon SageMaker Clarify B) Amazon SageMaker Ground Truth C) Amazon SageMaker Role Manager D) Amazon SageMaker Feature Store
B) Amazon SageMaker Ground Truth
Which AWS service is specifically designed for converting medical speech to text, ensuring compliance with healthcare regulations such as HIPAA? A) Amazon Polly B) Amazon Transcribe medical C) Amazon Rekognition D) Amazon Transcribe
B) Amazon Transcribe medical
What is the correct hierarchical relationship between Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI)? A) Generative AI > Deep Learning > Machine Learning > Artificial Intelligence B) Artificial Intelligence > Machine Learning > Deep Learning > Generative AI C) Artificial Intelligence > Generative AI > Machine Learning > Deep Learning D) Machine Learning > Deep Learning > Artificial Intelligence > Generative AI
B) Artificial Intelligence > Machine Learning > Deep Learning > Generative AI
How can you prevent model-overfitting in machine learning? A) By using techniques such as cross-validation, regularization, and pruning to simplify the model and improve its generalization B) By increasing the complexity of the model to ensure it captures all nuances in the training data C) By avoiding any form of model validation or testing to prevent the model from learning incorrect patterns D) By only training the model on a small subset of the available data to reduce the amount of information it has to learn
B) By increasing the complexity of the model to ensure it captures all nuances in the training data
Which prompt engineering technique helps break down a complex problem into smaller logical parts? A) Negative prompting B) Chain-of-thought prompting C) Zero shot Prompting D) Few shot Prompting
B) Chain-of-thought prompting
What is a key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning? A) Feature engineering for structured data focuses on image recognition, whereas for unstructured data, it focuses on numerical data analysis B) Feature engineering for structured data often involves tasks such as normalization and handling missing values, while for unstructured data, it involves tasks such as tokenization and vectorization C) Feature engineering tasks for structured data and unstructured data are identical and do not vary based on data type D) Feature engineering for structured data is not necessary as the data is already in a usable format, whereas for unstructured data, extensive preprocessing is always required
B) Feature engineering for structured data often involves tasks such as normalization and handling missing values, while for unstructured data, it involves tasks such as tokenization and vectorization
An insurance company is transitioning to AWS Cloud and wants to use Amazon Bedrock for product recommendations. The company wants to supplement organization-specific information to the underlying Foundation Model (FM). Which of these represents the best solution for the given use case? A) Use Knowledge Bases for Amazon Bedrock to supplement contextual information from the company's private data to the FM using Reinforcement Learning from Human Feedback (RLHF) B) Use Knowledge Bases for Amazon Bedrock to supplement contextual information from the company's private data to the FM using Retrieval Augmented Generation (RAG) C) Implement Reinforcement Learning from Human Feedback (RLHF) in Amazon Bedrock by leveraging the contextual information from the company's private data D) Fine-tune the base Foundation Model (FM) used by Amazon Bedrock by leveraging the contextual information from the company's private data
B) Use Knowledge Bases for Amazon Bedrock to supplement contextual information from the company's private data to the FM using Retrieval Augmented Generation (RAG)
Which of the following highlights the differences between model parameters and hyperparameters in the context of generative AI? A) Both Hyperparameters and model parameters are values that define a model and its behavior in interpreting input and generating responses B) Hyperparameters are values that define a model and its behavior in interpreting input and generating responses. Model parameters are values that can be adjusted for model customization to control the training process C) Model parameters are values that define a model and its behavior in interpreting input and generating responses. Hyperparameters are values that can be adjusted for model customization to control the training process D) Both Hyperparameters and model parameters are values that can be adjusted for model customization to control the training process
C) Model parameters are values that define a model and its behavior in interpreting input and generating responses. Hyperparameters are values that can be adjusted for model customization to control the training process
What is the key difference between SageMaker model cards and AI service cards? A) SageMaker model cards provide technical documentation for deploying models, while AI service cards offer transparency about the intended use, limitations, and potential impacts of AWS AI services B) SageMaker model cards are used to store data for machine learning models, while AI service cards are used for storing user credentials C) SageMaker model cards include information about the model such as intended use and risk rating of a model, training details and metrics, evaluation results, and observations. AI service cards provide transparency about AWS AI services' intended use, limitations, and potential impacts D) SageMaker model cards are used exclusively for monitoring model performance, whereas AI service cards are used for managing model security
C) SageMaker model cards include information about the model such as intended use and risk rating of a model, training details and metrics, evaluation results, and observations. AI service cards provide transparency about AWS AI services' intended use, limitations, and potential impacts
Which of the following options represent the CORRECT statements regarding the Amazon ML services? (Select two) A) Amazon Rekognition can extract key phrases and automatically organizes a collection of text files by topic B) Amazon Comprehend uses machine learning models to convert speech to text C) Amazon Transcribe is an AWS service for building conversational interfaces for applications using voice and text D) Amazon Polly is used to deploy high-quality, natural-sounding human voices in dozens of languages E) Amazon Comprehend service uses machine learning to find insights and relationships in the text
D) Amazon Polly is used to deploy high-quality, natural-sounding human voices in dozens of languages E) Amazon Comprehend service uses machine learning to find insights and relationships in the text
Which AWS service provides a visual point-and-click interface for analysts to solve business problems using Machine Learning? A) Amazon SageMaker Data Wrangler B) Amazon SageMaker Model Dashboard C) Amazon SageMaker Clarify D) Amazon SageMaker Canvas
D) Amazon SageMaker Canvas
Which Amazon SageMaker feature/utility provides information on the assumptions made while developing an ML model? A) Amazon SageMaker Model Monitor B) Amazon SageMaker Canvas C) Amazon SageMaker Model Cards D) Amazon SageMaker Clarify
D) Amazon SageMaker Clarify
What is a key difference between reinforcement learning and supervised learning? A) Reinforcement learning relies on learning from labeled datasets, whereas supervised learning involves an agent taking actions to receive rewards or penalties B) Reinforcement learning uses unlabeled data to cluster data points, whereas supervised learning uses labeled data to make predictions C) Reinforcement learning and supervised learning both require labeled datasets for training models D) Reinforcement learning focuses on an agent learning optimal actions through interactions with the environment and feedback, while supervised learning involves training models on labeled data to make predictions
D) Reinforcement learning focuses on an agent learning optimal actions through interactions with the environment and feedback, while supervised learning involves training models on labeled data to make predictions
What is the bias versus variance trade-off in machine learning? A) The bias versus variance trade-off involves choosing between a model with high complexity that may capture more noise (high bias) and a simpler model that may generalize better but miss important patterns (high variance) B) The bias versus variance trade-off is a technique used to improve model performance by increasing both bias and variance simultaneously to achieve better generalization C) The bias versus variance trade-off refers to the balance between underfitting and overfitting, where high bias leads to overfitting and high variance leads to underfitting D) The bias versus variance trade-off refers to the challenge of balancing the error due to the model's complexity (variance) and the error due to incorrect assumptions in the model (bias), where high bias can cause underfitting and high variance can cause overfitting
D) The bias versus variance trade-off refers to the challenge of balancing the error due to the model's complexity (variance) and the error due to incorrect assumptions in the model (bias), where high bias can cause underfitting and high variance can cause overfitting
