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

Question 1

A healthcare organization wants to use generative AI to draft clinical notes from patient-physician conversations. They must comply with HIPAA and minimize false medical information. Which approach should they take?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use Amazon Bedrock with a HIPAA-eligible foundation model and connect it to a medical knowledge base via RAG.. It combines a HIPAA-eligible foundation model via Amazon Bedrock with Retrieval-Augmented Generation (RAG) to ground responses in a curated medical knowledge base. This approach ensures compliance with HIPAA by using a service that supports Protected Health Information (PHI) processing under a Business Associate Agreement (BAA), while RAG reduces hallucination risk by retrieving factual clinical data rather than relying solely on the model's training.

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Use Amazon SageMaker JumpStart with a publicly available clinical model and no additional modifications.

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Use a generic open-source LLM hosted on Amazon EC2 with manual prompt engineering.

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Use Amazon Bedrock with a large foundation model and a high temperature setting for creativity.

Question 2

A startup wants to quickly prototype a generative AI application for summarizing news articles. They have limited ML expertise and want minimal infrastructure management. Which AWS service should they use?

Accepted Answer

Amazon Bedrock with a foundation model accessed via API.. Amazon Bedrock is the correct choice because it provides pre-trained foundation models from leading AI providers via a simple API, requiring no ML expertise or infrastructure management. The startup can quickly prototype a summarization application by sending news articles to the API and receiving summaries without training or deploying models.

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Amazon SageMaker to build and train a custom summarization model.

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AWS Lambda with a custom Python script using the Hugging Face Transformers library.

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Amazon EC2 instance running a pre-trained model from AWS Marketplace.

Question 3

A company is building a generative AI application to personalize email marketing campaigns. They use Amazon Bedrock with Anthropic Claude 3 Sonnet. The system takes customer data (name, purchase history) from an Amazon DynamoDB table and generates a personalized email body. During testing, the team notices that some emails contain factually incorrect information, such as recommending products the customer never purchased. The DynamoDB table is queried correctly and the correct data is passed to the model. The prompts include the customer data as context. The team has already tried adjusting the temperature and top-p parameters, but the issue persists.

They need to improve the factual accuracy of the generated emails without significantly increasing latency or cost. The application is currently deployed on a single AWS Lambda function that invokes Bedrock. The DynamoDB table is small (few thousand records).

Which course of action should the team take?

Accepted Answer

Use a structured prompt that explicitly instructs the model to base its response only on the provided customer data, and request a JSON object as output with the email body as a field.. The issue stems from the model not reliably adhering to the provided context. By using a structured prompt that explicitly instructs the model to base its response only on the given customer data and requesting JSON output, the team can enforce factual accuracy without significant overhead. This approach improves consistency and reduces hallucinations without increasing latency or cost. Option B is incorrect because a larger model may still produce inaccuracies and would increase cost and latency. Option C is incorrect because setting temperature to 0 reduces randomness but does not guarantee that the model will use the provided context correctly. Option D is incorrect because fine-tuning is resource-intensive and unnecessary for a small dataset; the problem is not a lack of domain knowledge but context adherence.

Answer

Switch to a larger model like Claude 3 Opus to improve accuracy.

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Reduce the temperature to 0 to make the model fully deterministic.

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Fine-tune the model on a dataset of correct email examples paired with customer data.

Question 4

A company is building a multi-step AI agent using Amazon Bedrock Agents to automate a complex business process that requires memory across interactions. The agent needs to remember user preferences and previous steps. Which approach best maintains state across sessions?

Accepted Answer

Leverage Amazon Bedrock Agents' built-in memory feature. Amazon Bedrock Agents includes a built-in memory feature that automatically maintains session state across multiple interactions, allowing the agent to recall user preferences and previous steps without requiring external storage or custom code. This is the most straightforward and efficient approach for multi-step AI agents that need persistent context.

Answer

Store state in AWS Lambda environment variables

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Use Amazon DynamoDB tables managed by Lambda functions

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Implement state management using AWS Step Functions

Question 5

A developer is building a customer-facing chatbot using Amazon Bedrock. To ensure the chatbot does not generate offensive or inappropriate content, which AWS feature should they implement?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it provides configurable safeguards that allow developers to define denied topics, content filters (e.g., hate, insults, sexual content), and sensitive information filters to prevent the model from generating offensive or inappropriate responses. Unlike prompt engineering or parameter tuning, Guardrails enforce policy-based constraints at inference time, independent of the underlying model's behavior.

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AWS Identity and Access Management (IAM) policies

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Prompt engineering with system prompts

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Increasing the model temperature parameter

Question 6

Which TWO actions would improve the grounding of responses from a generative AI model using RAG? (Choose 2)

Accepted Answer

Use RAG with a knowledge base of relevant documents. RAG (Retrieval-Augmented Generation) grounds the model's responses by retrieving relevant, up-to-date information from a curated knowledge base, ensuring factual accuracy and reducing hallucinations. Option E is correct because including source citations in the prompt instructions forces the model to reference specific retrieved documents, anchoring its output to verifiable data and improving grounding.

Answer

Fine-tune the model on unrelated data

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Reduce the context window to save tokens

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Increase the model's temperature parameter

Question 7

Which THREE are best practices for building a secure and scalable generative AI application using Amazon Bedrock? (Choose 3)

Accepted Answer

Implement guardrails to filter harmful content. Amazon Bedrock provides built-in guardrails that allow you to define and enforce content filters, deny topics, and sensitive information controls at the model invocation level. These guardrails help prevent the generation of harmful, biased, or inappropriate content, which is a critical security and compliance requirement for generative AI applications.

Answer

Deploy models on EC2 instances for better control

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Store API keys in source code for easy access

Question 8

Which TWO are benefits of using Amazon SageMaker JumpStart for foundation models? (Choose 2)

Accepted Answer

Built-in fine-tuning scripts and notebooks. Amazon SageMaker JumpStart provides built-in fine-tuning scripts and example notebooks that allow users to quickly adapt pre-trained foundation models to their specific datasets. These scripts handle common tasks like data preprocessing, hyperparameter tuning, and model deployment, significantly reducing the effort required to customize models.

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No coding required to fine-tune models

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Automatic scaling without any configuration

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Free unlimited usage for all models

Question 9

A financial services firm fine-tuned a generative AI model on Amazon SageMaker to summarize quarterly reports. The summaries often miss key financial metrics such as revenue and profit margins. The fine-tuning dataset contained full reports with summaries that included these metrics. The model appears to understand the reports but omits critical numbers. Which course of action would most likely improve the summaries?

Accepted Answer

Re-fine-tune using a carefully crafted dataset that includes explicit instructions to include key metrics and provides examples of correct summaries. The model's failure to include key financial metrics despite having them in the training data indicates a misalignment between the training objective and the desired output. By re-fine-tuning with a dataset that explicitly instructs the model to include key metrics and provides correct examples, you directly teach the model to prioritize and extract those specific numerical values during summarization, addressing the root cause of omission.

Answer

Increase the maximum number of tokens in the summary

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Switch to a different pre-trained model like Claude instead of the current one

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Implement a post-processing Lambda function that extracts metrics from the original report and appends them to the summary

Question 10

A company is building a generative AI application to generate product descriptions from customer reviews. They want to use Amazon Bedrock to access a foundation model. Which TWO actions should the company take to ensure responsible AI practices?

Accepted Answer

Implement human review of all generated descriptions before publication.. Implementing human review of all generated descriptions before publication ensures oversight of model outputs, catching potential biases, inaccuracies, or harmful content that automated systems might miss. This aligns with responsible AI practices by adding a human-in-the-loop to validate the foundation model's responses, which is critical for maintaining trust and safety in generative AI applications.

Answer

Use a single foundation model without any customization to avoid bias.

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Regularly evaluate model performance and fine-tune with diverse data.

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Disable content filtering to allow maximum creativity.

Question 11

A startup is building a customer support chatbot using Amazon Bedrock with the Claude foundation model. The chatbot needs to answer questions based on a knowledge base of frequently asked questions (FAQs) stored in an Amazon S3 bucket. The team wants to implement Retrieval Augmented Generation (RAG) to provide accurate and context-aware responses. They are evaluating different approaches to integrate the knowledge base. What is the most efficient way to implement RAG with Bedrock?

Accepted Answer

Use Amazon Bedrock Knowledge Bases to directly connect the S3 bucket and retrieve relevant documents for the prompt.. Amazon Bedrock Knowledge Bases provides a managed, native integration for connecting to data sources like S3, automatically chunking and indexing documents, and retrieving relevant context for RAG. This is the most efficient approach as it eliminates the need for custom code and manual prompt engineering. Option A is incorrect because using Lambda to fetch documents each time is less efficient and requires managing custom retrieval logic. Option B is impractical due to token limits and lack of dynamic updates. Option C is overkill and doesn't support real-time knowledge updates, making it less suitable for a frequently changing FAQ.

Answer

Use AWS Lambda to fetch documents from S3 and inject them into the prompt.

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Manually extract all FAQs and include them in the prompt each time the chatbot responds.

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Fine-tune the Claude model on the FAQs so the model memorizes the knowledge base.

Question 12

A financial services company wants to generate personalized investment recommendations using a large language model via Amazon Bedrock. They have customer data that includes risk tolerance, portfolio holdings, and financial goals. The company is highly concerned about data privacy and must avoid exposing sensitive personally identifiable information (PII) to the model. They plan to use a foundation model to generate recommendations based on customer profiles. What is the best approach to protect customer privacy while still enabling personalization?

Accepted Answer

Preprocess the customer data to replace sensitive fields with placeholders, then use the processed data in the prompt.. Preprocessing customer data to replace sensitive fields with placeholders (e.g., using synthetic IDs) allows the model to generate personalized recommendations without accessing real PII. This minimizes risk. Option A is incorrect because fine-tuning on a large dataset of generic recommendations does not produce personalized outputs for individual customers. Option B is incorrect because prompt engineering instructions are not a robust privacy control and cannot reliably prevent PII exposure. Option D is incorrect because including customer data directly in the prompt and relying on the model to anonymize it is unreliable and may still leak PII.

Answer

Fine-tune the model on a large dataset of investment recommendations without any customer-specific data.

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Use prompt engineering to instruct the model to disregard any personally identifiable information.

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Include the customer data directly in the prompt and rely on the model to anonymize it.

Question 13

A research lab is using Amazon SageMaker to fine-tune a large language model (LLM) for scientific text summarization. The training dataset contains 10 million documents, and the lab has a limited budget but needs to minimize training time. They have access to SageMaker Training with managed spot instances, which offer significant cost savings but are interruptible. The team is considering different training strategies to balance cost, time, and model quality. Which strategy should they use?

Accepted Answer

Use SageMaker's distributed training with data parallelism on multiple managed spot instances, and enable checkpointing.. The best strategy. Using SageMaker's distributed training with data parallelism across multiple managed spot instances allows parallel processing of the 10-million-document dataset, significantly reducing training time. Spot instances offer cost savings of up to 90% compared to on-demand, and enabling checkpointing ensures that if any instance is interrupted, training can resume from the last checkpoint without losing progress. Option B is incorrect because fine-tuning only the last few layers on a subset of data compromises model quality and does not leverage the full dataset. Option C is incorrect because a single on-demand instance is expensive and slower than distributed spot instances. Option D is incorrect because training from scratch on a single GPU is prohibitively slow and costly, especially given the budget constraints.

Answer

Fine-tune only the last few layers of the model on a smaller subset of the data.

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Use a single on-demand instance to avoid interruptions and maximize throughput.

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Use a single large GPU instance to train the model from scratch.

Question 14

Which TWO of the following are key advantages of using Amazon Bedrock for building generative AI applications?

Accepted Answer

Ability to fine-tune models using your own data without managing underlying infrastructure.. Amazon Bedrock allows you to fine-tune foundation models using your own labeled datasets without provisioning or managing any underlying GPU instances or infrastructure. This serverless approach abstracts away the complexity of training infrastructure, enabling you to customize models for domain-specific tasks while AWS handles scaling, patching, and resource allocation. Option E is correct because Amazon Bedrock provides access to multiple foundation models from different providers (e.g., Anthropic, AI21 Labs, Stability AI) via a single API, simplifying model selection and integration for diverse generative AI use cases.

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Automatic optimization of prompts for all models without user intervention.

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Eliminates the need for any data preprocessing before model invocation.

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Guaranteed identical outputs from all models for the same prompt.

Question 15

Refer to the exhibit. A developer has attached this IAM policy to their user. When trying to invoke the Anthropic Claude v2 model using the Bedrock runtime, they receive an AccessDeniedException. Which change to the policy would resolve the issue?

Accepted Answer

Change the Resource to arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2. The IAM policy's Resource element must specify the exact ARN of the foundation model being invoked. The original policy likely used a wildcard or incorrect ARN pattern. The correct ARN for Anthropic Claude v2 in us-east-1 is arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2, which grants permission to invoke that specific model via the Bedrock runtime.

Answer

Add the bedrock:InvokeModelWithResponseStream action

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Change the Action to bedrock:ListFoundationModels

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Remove the Resource element and set Effect to Deny