AIF-C01 Fundamentals of Generative AI Practice Test 7 — 15 Questions

Question 1

A healthcare startup is using Amazon Bedrock to generate clinical notes. They must prevent the model from outputting any personally identifiable information (PII) such as patient names. What is the most effective approach?

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

Accepted Answer

Configure a guardrail in Amazon Bedrock to deny PII topics. Amazon Bedrock Guardrails provide a native, policy-based mechanism to deny the generation of PII topics at inference time, without requiring model retraining or external filtering. This approach directly intercepts and blocks prohibited content before it is returned, offering the most reliable and maintainable solution for compliance with healthcare privacy regulations like HIPAA.

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Fine-tune the model on de-identified data only

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Use a prompt engineering technique to instruct the model to avoid PII

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Post-process the output with a regex filter

Question 2

A developer invoked an Amazon Bedrock model and received this output. What does the stopReason field indicate?

Accepted Answer

The model reached the maximum token limit set in the request. The `stopReason` field in Amazon Bedrock model responses indicates why the model stopped generating tokens. When set to `end_turn` or `stop_sequence`, it means the model reached a natural stopping point or encountered a user-defined stop sequence. However, if the value is `max_tokens`, it explicitly means the model stopped because it reached the maximum token limit specified in the `max_tokens` request parameter. Option C correctly identifies this behavior.

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A content filter blocked the output

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The input prompt was too long

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The model reached a natural stopping point

Question 3

A company wants to use Amazon Bedrock to generate images from text descriptions. Which model should they use?

Accepted Answer

Amazon Titan Image Generator. Amazon Titan Image Generator is the correct choice because it is specifically designed by AWS for generating images from text descriptions within Amazon Bedrock. It leverages a diffusion-based architecture to create high-quality images, making it the native and optimal model for this task on the platform.

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Stable Diffusion XL

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Amazon Titan Text

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Amazon Polly

Question 4

A developer invoked an Amazon Bedrock model and received the following error: 'ValidationException: 1 validation error detected: Value 'claude-instant-v1' at 'modelId' failed to satisfy constraint: Member must satisfy enum value set: [ai21.j2-mid-v1, amazon.titan-text-lite-v1, anthropic.claude-v2, ...]'. What is the likely cause?

Accepted Answer

The modelId is not part of the allowed enum of models for the account. The error message explicitly states that the value 'claude-instant-v1' at 'modelId' failed to satisfy the constraint: 'Member must satisfy enum value set'. This indicates that the modelId provided is not part of the allowed list of model identifiers that the Amazon Bedrock API accepts for the invocation request. The error is a validation error from the API itself, not a permissions or availability issue, meaning the modelId string does not match any entry in the predefined enum of supported models.

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The Lambda function does not have the necessary IAM permissions

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The modelId is not available in the current AWS region

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The modelId is deprecated and has been renamed

Question 5

A developer is building a chatbot using Amazon Bedrock and Claude. They notice that the model sometimes generates harmful or biased responses. Which AWS service can they use to implement guardrails?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is a native feature of Amazon Bedrock designed specifically to implement safety controls, content filters, and topic policies for foundation models like Claude. It allows developers to define denied topics, filter harmful content (e.g., hate speech, violence), and redact sensitive information, directly addressing the need to prevent harmful or biased responses in a chatbot built on Bedrock.

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AWS WAF

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Amazon GuardDuty

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AWS Shield

Question 6

A company wants to use a large language model to generate code based on natural language descriptions. They need to minimize latency and control costs by running inference on their own infrastructure. Which approach is most suitable?

Accepted Answer

Use Amazon SageMaker to deploy a custom LLM. Amazon SageMaker allows you to deploy a custom large language model (LLM) on your own infrastructure, giving you full control over inference latency and cost. By using SageMaker endpoints with auto-scaling and instance selection, you can optimize for low-latency responses while avoiding per-token API charges from managed services.

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Use Amazon Bedrock API

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Use Amazon Comprehend

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Use Amazon Lex

Question 7

A developer is building an application that generates product descriptions from images using a multimodal model. Which AWS service provides access to multimodal foundation models?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a managed service that provides access to a wide range of foundation models (FMs) from leading AI providers, including multimodal models that can process both images and text to generate product descriptions. This makes it the correct choice for building an application that requires multimodal capabilities.

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Amazon Rekognition

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Amazon Textract

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Amazon Comprehend

Question 8

Which TWO AWS services can be used to build a chatbot that responds to customer inquiries using a company's documentation as source? (Select two.)

Accepted Answer

Amazon Bedrock with RAG. Amazon Bedrock with RAG (Retrieval-Augmented Generation) enables a chatbot to retrieve relevant chunks from a company's documentation and use them as context for a foundation model to generate accurate, grounded responses. This approach directly addresses the need to answer customer inquiries using proprietary or domain-specific source material.

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Amazon Polly

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Amazon Transcribe

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Amazon Lex

Question 9

Refer to the exhibit. A developer is optimizing latency for a generative AI model deployed on SageMaker. Based on the exhibit, which change would most likely reduce per-token latency?

Accepted Answer

Reduce model size through quantization. Reducing model size through quantization directly decreases the computational and memory requirements per inference step, which lowers the time to generate each token. This is especially effective on GPU instances where smaller models fit better in GPU memory and reduce memory bandwidth bottlenecks, leading to lower per-token latency.

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Use a CPU instance

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Switch to a larger instance type

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Increase batch size to 10

Question 10

A developer wants to generate product description images using Amazon Bedrock. They need to ensure the generated images match a specific brand style. Which feature should they primarily use?

Accepted Answer

Prompt engineering with detailed style descriptions.. Prompt engineering with detailed style descriptions is the primary and most direct method to guide Amazon Bedrock's image generation models (e.g., Stable Diffusion, Titan Image Generator) toward a specific brand style. By crafting precise prompts that include brand colors, design elements, and stylistic cues, the developer can influence the output without requiring additional training data or model modifications.

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Output grounding to verify brand compliance.

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Data augmentation to increase dataset diversity.

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Fine-tuning the image generation model on brand assets.

Question 11

A financial services company is subject to strict regulatory requirements. They plan to use generative AI to summarize customer interaction logs. Which combination of AWS services and configurations best ensures compliance while maintaining accuracy?

Accepted Answer

Use Amazon Bedrock with a private VPC endpoint, AWS KMS encryption, and content filtering.. It combines a private VPC endpoint to keep all traffic within the AWS network (avoiding public internet exposure), AWS KMS encryption for data at rest and in transit, and content filtering to block sensitive or non-compliant outputs. This architecture meets strict regulatory requirements for data privacy and security while using Amazon Bedrock's managed foundation models for accurate summarization.

Answer

Deploy an open-source model on Amazon Bedrock in a local on-premises server.

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Use Amazon Bedrock with a foundation model and public internet access without encryption.

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Use Amazon SageMaker to host a fine-tuned model with a public API key.

Question 12

A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?

Accepted Answer

Select a smaller foundation model that still meets accuracy requirements.. The most effective cost-reduction strategy because smaller foundation models (FMs) have fewer parameters, resulting in lower compute and inference costs per request. If the smaller model still meets the required accuracy benchmarks for the marketing content task, it directly reduces operational expenditure without sacrificing quality. Amazon Bedrock offers a range of FMs (e.g., from large models like Claude 3 Opus to smaller ones like Claude 3 Haiku), allowing you to match model size to task complexity.

Answer

Fine-tune a larger model to improve accuracy and reduce retries.

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Increase the temperature parameter to get shorter responses.

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Cache previous responses to reuse for similar prompts.

Question 13

A developer is using Amazon Bedrock to create a chatbot. They want to ensure the bot does not generate toxic or offensive content. Which feature should they enable?

Accepted Answer

Enable content filtering on the Bedrock model.. Amazon Bedrock provides built-in content filtering capabilities that can be enabled at the model invocation level to automatically detect and block toxic or offensive content in both input prompts and generated responses. This feature uses predefined safety filters (e.g., hate, insults, sexual content, violence) and is the most direct and managed way to prevent harmful outputs without requiring custom development.

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Use careful prompt engineering to avoid toxic responses.

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Fine-tune the model on a dataset of safe responses.

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Implement external response validation using a third-party API.

Question 14

A company operates in a region where Amazon Bedrock is not available. They want to use generative AI but must keep data within the country. Which solution should they consider?

Accepted Answer

Use Amazon SageMaker to host an open-source model in the local region.. Amazon SageMaker allows you to host open-source models (e.g., Llama 2, Falcon) in any AWS region, including those where Bedrock is unavailable. This satisfies the data residency requirement because the model and data never leave the local region. SageMaker provides full control over the infrastructure, enabling compliance with local data sovereignty laws.

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Wait for Bedrock to become available in their region; there is no alternative.

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Use Amazon Bedrock in the nearest available region with cross-region inference.

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Use an API from a third-party generative AI provider with AWS PrivateLink.

Question 15

Which TWO actions can help reduce the likelihood of hallucinations in a generative AI model used for question answering?

Accepted Answer

Use Retrieval Augmented Generation (RAG) with a trusted knowledge base.. Retrieval Augmented Generation (RAG) grounds the model's responses in a trusted, external knowledge base, providing factual context that reduces the model's reliance on its parametric memory alone. By retrieving relevant documents at inference time, RAG directly mitigates the risk of hallucination, as the model generates answers based on retrieved evidence rather than inventing information.

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Increase the maximum token count to allow more complete answers.

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Fine-tune the model on the training data used for the application.

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Use a larger foundation model with more parameters.