AIF-C01 Fundamentals of Generative AI Practice Test 6 — 15 Questions

Question 1

A developer wants to test different prompt variations for a chatbot without making repeated API calls. Which Amazon Bedrock feature can help compare model responses?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground provides an interactive console interface where developers can test and compare different prompt variations, model configurations, and foundation models side-by-side without making repeated API calls. It allows real-time experimentation with parameters like temperature, top-p, and prompt engineering to observe how the model responds, making it the correct choice for this use case.

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Model evaluation on Amazon SageMaker

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AWS Security Token Service (STS)

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Amazon CloudWatch Logs

Question 2

A company is deploying a customer-facing chatbot using Amazon Bedrock. They want to reduce the risk of generating biased or harmful responses. Which TWO measures should they implement? (Choose 2.)

Accepted Answer

Implement a human-in-the-loop review for sensitive replies. Option A is correct because implementing a human-in-the-loop review for sensitive replies adds a manual verification layer that can catch biased or harmful outputs before they reach customers, which is a recommended responsible-AI practice for customer-facing generative applications. Option C is correct because Amazon Bedrock Guardrails lets you configure content filters (for hate, insults, sexual, violence, misconduct), denied topics, word filters, and contextual grounding checks that automatically block or mask harmful or biased responses at inference time. Option B is not appropriate because training exclusively on historical customer conversations can perpetuate existing biases and does not by itself mitigate harmful output. Option D is incorrect because setting temperature to 1.5 increases randomness and creativity, making outputs less predictable and potentially more harmful. Option E is incorrect because disabling logging reduces observability and auditability, which undermines monitoring and continuous improvement of safety controls.

Answer

Train the model exclusively on historical customer conversations

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Set the temperature parameter to 1.5

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Disable logging to improve performance

Question 3

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 4

Which AWS service provides a fully managed experience for building generative AI applications with a variety of foundation models through a unified API?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is the correct answer because it is a fully managed AWS service that provides access to a variety of foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single, unified API. This allows developers to build and scale generative AI applications without managing underlying infrastructure or model hosting, directly addressing the requirement for a fully managed experience with diverse FMs.

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AWS Lambda

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Amazon SageMaker

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Amazon Rekognition

Question 5

A company is using Amazon Bedrock to generate marketing copy. They want to ensure the model's responses are factually accurate and grounded in their proprietary knowledge base. Which feature should they use?

Accepted Answer

Retrieval Augmented Generation (RAG). Retrieval Augmented Generation (RAG) is the correct choice because it retrieves relevant documents from the company's proprietary knowledge base and provides them as context to the foundation model at inference time. This grounds the model's responses in factual, up-to-date information without modifying the underlying model weights, ensuring accuracy and reducing hallucinations.

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Model customization

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Fine-tuning

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Prompt engineering

Question 6

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 7

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 8

A data scientist wants to fine-tune a foundation model on a specific domain dataset using Amazon SageMaker. Which built-in SageMaker feature can simplify the training process?

Accepted Answer

SageMaker JumpStart. SageMaker JumpStart provides pre-trained foundation models and built-in training scripts that simplify fine-tuning on custom datasets. It handles the underlying infrastructure, hyperparameter configurations, and model deployment, allowing the data scientist to focus on the domain-specific data rather than writing custom training loops or managing SageMaker training jobs manually.

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SageMaker Neo

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SageMaker Canvas

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SageMaker Ground Truth

Question 9

A company is using Amazon Bedrock to summarize long documents. They notice that the summary sometimes omits key details. What is the most likely cause?

Accepted Answer

The model's context window is too small. When summarizing long documents with Amazon Bedrock, the model's context window determines the maximum amount of text it can process at once. If the document exceeds this limit, the model truncates or ignores portions, leading to omitted key details. This is the most likely cause because summarization requires the model to attend to the entire input, and a small context window directly prevents full coverage.

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The model is overfitted

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The prompt lacks examples

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The temperature parameter is too high

Question 10

An enterprise wants to ensure that generative AI applications built on AWS comply with data privacy regulations. They need to prevent the model from using customer data in future training. Which feature of Amazon Bedrock should they enable?

Accepted Answer

Opt-out of model improvement. Amazon Bedrock's opt-out of model improvement feature allows customers to prevent AWS from using their data (including prompts, completions, and associated metadata) for model training or service improvement. This is essential for compliance with data privacy regulations like GDPR or CCPA, as it ensures customer data is not retained or used beyond the immediate inference request.

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Policy-based data governance

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Data encryption at rest

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Model customization with customer data

Question 11

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 12

A team is developing a real-time code completion feature using an LLM deployed on Amazon SageMaker. They observe high latency under load. Which optimization technique should they prioritize?

Accepted Answer

Use model quantization. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and computational requirements, leading to lower latency per inference. This is the most effective optimization for real-time code completion because it directly reduces the time to generate each token without requiring additional infrastructure changes.

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Increase batch size

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Switch to a larger instance type

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Increase instance count with Auto Scaling

Question 13

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 (A) is correct because it lets you build generative AI chatbots that retrieve relevant passages from your company documentation (stored in services like Amazon S3 or OpenSearch) and pass them to a foundation model as context, so answers are grounded in the source material. Amazon Q Business (C) is correct because it is a fully managed generative AI assistant that natively connects to enterprise data sources (S3, SharePoint, Confluence, etc.), indexes the documentation, and answers customer inquiries with citations from that content. Amazon Polly (B) is only a text-to-speech service and cannot retrieve or reason over documentation, Amazon Transcribe (D) only converts speech to text, and Amazon Lex (E) builds conversational interfaces/intents but does not itself perform retrieval-augmented generation over a company's documentation.

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Amazon Polly

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Amazon Transcribe

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Amazon Lex

Question 14

A company wants to evaluate the performance of a generative AI model before deployment. Which TWO metrics are most relevant for measuring model quality? (Select two.)

Accepted Answer

BLEU score. BLEU score (A) is correct because it measures the quality of generated text by comparing n-gram overlap between model output and reference text, making it a standard metric for evaluating generative AI models such as translation and summarization systems. Perplexity (C) is correct because it quantifies how well a language model predicts a sample of text, with lower perplexity indicating better model confidence and language modeling quality. Response time (B) is not a quality metric but a latency/performance measure, model size (D) reflects resource footprint rather than output quality, and CPU utilization (E) is an infrastructure efficiency metric unrelated to the model's generative quality.

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Response time

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Model size

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CPU utilization

Question 15

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