AIF-C01 Online Practice Questions and Answers

Correct Answer: C

Amazon Bedrock is a service that provides foundational models for building generative AI applications. When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience.

"Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels. Option C (Correct): "Guardrails for Amazon Bedrock":This is the

correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children's application, guardrails ensure that all

content generated is suitable and appropriate for the intended audience. Option A:"Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it

does not handle text or story generation. Option B:"Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure

content appropriateness for specific audiences, such as children.

Option D:"Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.

AWS AI Practitioner References:

Guardrails in Amazon Bedrock:Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.

Building Safe AI Applications:AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.

Correct Answer: D

The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost- effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have

been developed to minimize environmental impact by maximizing energy efficiency. Option D (Correct): "Amazon EC2 Trn series":This is the correct answer because the Trn series is purpose-built for training deep learning models with lower

energy consumption, which aligns with the company's concern about environmental effects.

Option A:"Amazon EC2 C series" is incorrect because it is intended for compute- intensive tasks but not specifically optimized for ML training with environmental considerations.

Option B:"Amazon EC2 G series" (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.

Option C:"Amazon EC2 P series" is designed for ML training but does not offer the same level of energy efficiency as the Trn series.

AWS AI Practitioner References:

AWS Trainium Overview:AWS promotes Trainium instances as their most energy- efficient and cost-effective solution for ML model training.

Correct Answer: A

Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for

validation purposes. Option A (Correct): "Amazon S3":This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.

Option B:"Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.

Option C:"Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances. Option D:"AWS Snowcone" is incorrect because it is a physical device for offline data transfer,

not suitable for directly providing data to Amazon Bedrock.

AWS AI Practitioner

References:

Storing and Managing Datasets on AWS for Machine Learning:AWS recommends using S3 for storing and managing datasets required for ML model training and validation.

Correct Answer: A

Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating

parts of the software development process.

Option A (Correct): "Create software snippets, reference tracking, and open- source license tracking":This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.

Option B:"Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer. Option C:"Enable voice commands for coding and providing natural language

search" is incorrect because this is not a function of Amazon Q Developer. Option D:"Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.

AWS AI Practitioner References:

Amazon Q Developer Features:AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

Correct Answer: B

Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.

Correct Answer: B

Unsupervised learning is the correct methodology for classifying customers into tiers when the data is unlabeled, as it does not require predefined labels or outputs.

Correct Answer: B

Reinforcement learning allows a model to learn and improve over time based on feedback from its environment. In this case, the chatbot can improve its responses by being rewarded for positive customer feedback, which aligns well with the

goal of self- improvement based on past interactions and new information. Option B (Correct): "Reinforcement learning with rewards for positive customer feedback":This is the correct answer as reinforcement learning enables the chatbot to

learn from feedback and adapt its behavior accordingly, providing self- improvement capabilities.

Option A:"Supervised learning with a manually curated dataset" is incorrect because it does not support continuous learning from new interactions. Option C:"Unsupervised learning to find clusters of similar customer inquiries" is incorrect

because unsupervised learning does not provide a mechanism for improving responses based on feedback.

Option D:"Supervised learning with a continuously updated FAQ database" is incorrect because it still relies on manually curated data rather than self- improvement from feedback.

AWS AI Practitioner

References:

Reinforcement Learning on AWS:AWS provides reinforcement learning frameworks that can be used to train models to improve their performance based on feedback.

Correct Answer: A

Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.

Correct Answer: A

Batch transform in Amazon SageMaker is designed for offline processing of large datasets. It is ideal for scenarios where immediate predictions are not required, and the inference can be done on large datasets that are multiple gigabytes in

size. This method processes data in batches, making it suitable for analyzing archived data without the need for real- time access to predictions.

Option A (Correct): "Batch transform":This is the correct answer because batch transform is optimized for handling large datasets and is suitable when immediate access to predictions is not required.

Option B:"Real-time inference" is incorrect because it is used for low-latency, real- time prediction needs, which is not required in this case. Option C:"Serverless inference" is incorrect because it is designed for small-scale, intermittent

inference requests, not for large batch processing. Option D:"Asynchronous inference" is incorrect because it is used when immediate predictions are required, but with high throughput, whereas batch transform is more suitable for very large

datasets.

AWS AI Practitioner

References:

Batch Transform on AWS SageMaker:AWS recommends using batch transform for large datasets when real-time processing is not needed, ensuring cost- effectiveness and scalability.

Correct Answer: A

Tokens in generative AI models are the smallest units that the model processes, typically representing words, subwords, or characters. They are essential for the model to understand and generate language, breaking down text into

manageable parts for processing.

Option A (Correct): "Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units":This is the correct definition of tokens in the context of generative AI models.

Option B:"Mathematical representations of words" describes embeddings, not tokens.

Option C:"Pre-trained weights of a model" refers to the parameters of a model, not tokens.

Option D:"Prompts or instructions given to a model" refers to the queries or commands provided to a model, not tokens.

AWS AI Practitioner

References:

Understanding Tokens in NLP:AWS provides detailed explanations of how tokens are used in natural language processing tasks by AI models, such as in Amazon Comprehend and other AWS AI services.