AIF-C01 Fundamentals of Generative AI Practice Test 4 — 15 Questions

Question 1

A company wants to generate product descriptions from a few keywords without managing infrastructure. Which AWS service provides a serverless API for accessing foundation models?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed, serverless service that provides a single API to access and invoke foundation models (FMs) from leading AI providers like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI. It eliminates the need to manage underlying infrastructure, making it the correct choice for generating product descriptions from keywords without provisioning servers.

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Amazon Lex

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Amazon SageMaker

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Amazon Comprehend

Question 2

A data scientist is evaluating foundation models for a text summarization task and wants to use a standard metric. Which metric is commonly used to assess the quality of generated summaries?

Accepted Answer

ROUGE. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is the standard metric for text summarization. It measures the overlap of n-grams, word sequences, or word pairs between the generated summary and reference summaries, focusing on recall. This makes it particularly suitable for evaluating how well the generated summary captures the key content of the reference.

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F1 score

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BLEU

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Accuracy

Question 3

A company is building a chatbot that must provide accurate answers based on internal documents without retraining the model. Which approach should they use?

Accepted Answer

Prompt engineering with retrieval-augmented generation (RAG). Retrieval-augmented generation (RAG) allows the chatbot to fetch relevant internal documents at inference time and incorporate them into the prompt, providing accurate, up-to-date answers without retraining the model. This approach combines prompt engineering with a retrieval step, ensuring the model's responses are grounded in the company's specific knowledge base while keeping the base model frozen.

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Reinforcement learning from human feedback (RLHF)

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Fine-tuning the model on internal documents

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Model distillation to a smaller model

Question 4

A developer wants to test different foundation models quickly without setting up infrastructure. Which AWS service allows interactive prompting and comparison of multiple models?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is a feature within Amazon Bedrock that provides a web-based interface for interactive prompting and side-by-side comparison of multiple foundation models (FMs). It allows developers to test different models quickly without provisioning any infrastructure, making it ideal for rapid experimentation and evaluation.

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Amazon Comprehend

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Amazon Lex

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Amazon SageMaker Studio

Question 5

A machine learning engineer notices that a generative AI model occasionally produces biased outputs. Which AWS feature can automatically filter harmful content before it reaches users?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is specifically designed to implement safeguards for generative AI applications, including the ability to filter harmful, biased, or inappropriate content before it reaches users. It allows you to define denied topics, content filters, and sensitive information filters that are applied at inference time, directly addressing the need to automatically filter biased outputs from a generative AI model.

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Amazon CloudWatch alarms

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Amazon SageMaker Clarify

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AWS Identity and Access Management (IAM) policies

Question 6

A team is using Amazon Bedrock with a Claude model and wants to ensure responses adhere to a specific output format such as JSON. Which technique should be applied?

Accepted Answer

Include a system prompt with explicit formatting instructions. Amazon Bedrock with Claude models supports system prompts that can include explicit formatting instructions, such as 'Respond in valid JSON format.' This technique directly controls the model's output structure without requiring external tools or retraining, making it the simplest and most effective method for enforcing a specific output format like JSON.

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Use a retrieval-augmented generation (RAG) approach

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Attach a guardrail with a JSON schema

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Customize the model with a JSON training dataset

Question 7

A startup wants to integrate a generative AI chatbot into their mobile app with minimal latency. Which AWS service is purpose-built for deploying foundation models with low latency and high throughput?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from leading AI providers via a serverless API, purpose-built for deploying generative AI applications with low latency and high throughput. It handles the underlying infrastructure, model hosting, and scaling automatically, making it ideal for integrating a generative AI chatbot into a mobile app with minimal latency.

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AWS Lambda

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Amazon SageMaker

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Amazon Transcribe

Question 8

A company wants to personalize its generative AI model for its specific domain without sharing data with third-party model providers. Which method should they use?

Accepted Answer

Fine-tuning the foundation model on their proprietary data. Fine-tuning the foundation model on proprietary data allows the company to adapt the model's weights to its specific domain without sharing data with third parties. This method trains the model on private datasets, enabling it to learn domain-specific patterns and terminology while keeping data in-house, which is critical for data privacy and compliance.

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Prompt engineering with domain-specific examples

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Retrieval-augmented generation (RAG) with a domain-specific knowledge base

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Model distillation using a larger foundation model

Question 9

A developer is using Amazon Bedrock's Converse API to build a multi-turn conversation. They notice the model forgets earlier context after a few exchanges. What is the most likely cause?

Accepted Answer

The developer is not sending the previous messages in each request. The Converse API is stateless by design, meaning it does not retain conversation history between API calls. To maintain context across multiple turns, the developer must explicitly include the entire message history (previous user and assistant messages) in each new request. If the developer omits these previous messages, the model has no memory of earlier exchanges and will appear to forget context.

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The API has a rate limit that truncates history

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The model's context window is too small for the conversation

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The model's maximum output length is set too low

Question 10

Which TWO actions are best practices for reducing hallucinations in generative AI models? (Choose 2)

Accepted Answer

Use retrieval-augmented generation (RAG). Option C, retrieval-augmented generation (RAG), is correct because it grounds the model's responses in externally retrieved, authoritative documents at inference time, so the model cites or conditions on real source content rather than relying solely on parametric memory, which measurably reduces fabricated facts. Option E, prompt engineering with clear instructions and constraints, is correct because explicit directives such as "answer only from the provided context," "say 'I don't know' if unsupported," and output-format constraints steer the model away from speculative generation and make hallucinations easier to detect. Option A, increasing model size, is not a reliable fix: larger models can be more fluent and confident while still hallucinating, and scale alone does not guarantee factual grounding. Option B, fine-tuning on proprietary data, mainly adapts style, format, and domain vocabulary and can even increase confident fabrication on facts not present in the tuning set, so it is not a primary hallucination-reduction best practice. Option D, using a smaller model, is also not a best practice for this goal, since reduced capacity generally lowers factual accuracy and reasoning rather than improving truthfulness.

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Increase the model size

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Fine-tune the model on proprietary data

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Use a smaller model to limit complexity

Question 11

Which THREE are key capabilities of Amazon Bedrock? (Choose 3)

Accepted Answer

Model customization through fine-tuning. Amazon Bedrock provides model customization through fine-tuning (B), allowing you to adapt supported foundation models with your own labeled data to improve performance for domain-specific tasks. It also offers Guardrails for Amazon Bedrock (C), which let you define policies that filter harmful or inappropriate content and enforce topics and sensitive-information redaction across model responses. Bedrock additionally delivers serverless inference for foundation models (D), so you can invoke models via a managed API without provisioning or managing any underlying infrastructure. Option A is not a Bedrock capability because Bedrock does not automatically choose a model for you; the developer selects the model. Option E is incorrect because Bedrock itself does not include a built-in vector database; knowledge bases for Amazon Bedrock integrate with separate vector stores such as Amazon OpenSearch Serverless or Amazon Aurora.

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Automatic model selection based on use case

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Built-in vector database for knowledge bases

Question 12

A developer runs this AWS CLI command to invoke a model in us-west-2 but receives an error: 'An error occurred (ModelNotFoundException) when calling the InvokeModel operation: Model not found'. What is the most likely cause?

Accepted Answer

The model is not available in the us-west-2 region. The error 'ModelNotFoundException' specifically indicates that the model ID or ARN is not recognized in the specified region. AWS Bedrock models are region-specific; not all foundation models are available in every AWS region. The developer invoked the model in us-west-2, but the requested model may only be available in regions like us-east-1 or us-west-1, causing the service to return a ModelNotFoundException rather than a permissions or formatting error.

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The request body is not properly formatted

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The --region parameter is missing from the command

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The user's IAM role lacks permissions to invoke the model

Question 13

A developer deployed this guardrail to block sensitive topics and sexual content. However, the model still generates responses about a specific sensitive topic that is not in the TopicPolicy. What should the developer do to prevent this?

Accepted Answer

Add the specific topic to the TopicPolicy list. The guardrail's TopicPolicy is designed to block specific topics by listing them. Since the model is generating responses about a sensitive topic not currently in the policy, adding that topic to the list directly extends the policy's coverage to prevent those responses. This is the precise mechanism for blocking new unwanted topics without altering other guardrail components.

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Add a SensitiveInformationPolicy to filter PII

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Increase the InputStrength of the content filter to MAX

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Change the TopicPolicy Type from DENY to ALLOW

Question 14

A retail company wants to generate product descriptions from catalog data. The data includes structured attributes (e.g., price, brand) and unstructured reviews. The team needs to ensure factual accuracy. Which approach is most appropriate?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) with a knowledge base. Retrieval-Augmented Generation (RAG) retrieves relevant documents (product attributes, reviews) and provides them as context to the model, reducing hallucinations and grounding responses in facts.

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Use prompt engineering with few-shot examples

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Fine-tune a foundation model on the entire product catalog

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Deploy a larger foundation model with more parameters

Question 15

Which AWS service provides a serverless experience for building and scaling generative AI applications with access to various foundation models?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides a serverless experience for building and scaling generative AI applications. It offers access to a variety of foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, without the need to manage underlying infrastructure.

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Amazon SageMaker

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Amazon Lex

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AWS Lambda