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

Question 1

Refer to the exhibit. A developer wants to choose a model that can generate text (not just embeddings) and has the lowest cost. Based on the exhibit, which model should they select?

Accepted Answer

Titan Text Lite. Titan Text Lite is the correct choice because it is designed for text generation tasks and is explicitly positioned as the lowest-cost option among Amazon's Titan text generation models. Unlike Titan Embed Text, which only produces embeddings and cannot generate text, Titan Text Lite offers a cost-efficient solution for generating text while meeting the requirement of lowest cost.

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Titan Embed Text

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Titan Text Express

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Need more information

Question 2

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 3

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.

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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 4

A company is using Amazon Bedrock to generate code snippets. They notice the model occasionally generates code that fails to compile. What is the most effective way to improve code quality without retraining?

Accepted Answer

Use few-shot prompt engineering with correct code examples and formatting instructions.. Few-shot prompt engineering provides the model with explicit examples of correct code and formatting instructions, guiding it to generate syntactically valid code without modifying the underlying model. This approach leverages in-context learning to improve output quality by conditioning the model on desired patterns, which is more effective than parameter adjustments alone for addressing compilation errors.

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Reduce the temperature parameter to 0 for deterministic output.

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Increase the max token limit to allow the model to complete the code fully.

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Fine-tune the model on a dataset of correct code snippets.

Question 5

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?

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 6

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 7

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.

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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 8

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 9

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 10

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.. Option B is correct because Retrieval Augmented Generation (RAG) grounds the model's responses in documents retrieved from a trusted knowledge base, so answers are conditioned on verifiable source content rather than the model's parametric memory, which substantially reduces fabricated or unsupported statements. Option D is correct because lowering the temperature parameter (e.g., to 0.1) sharpens the next-token probability distribution, making the model select high-probability, more deterministic tokens instead of sampling low-probability alternatives that often produce invented facts. Option A does not belong because increasing the maximum token count only allows longer outputs; it does not improve factual grounding and can even give hallucinations more room to expand. Option C does not belong because fine-tuning on the application's training data can reinforce patterns and biases in that data and does not guarantee factual accuracy, and may even increase confident hallucination. Option E does not belong because a larger foundation model with more parameters may be more fluent but is not inherently less prone to hallucination, since scale alone does not provide source grounding or reduce sampling randomness.

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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.

Question 11

Which TWO factors are most important when selecting a foundation model in Amazon Bedrock for a text summarization task with strict latency requirements?

Accepted Answer

Average response latency per request.. Option A is correct because with strict latency requirements the deciding metric is the model's average response latency per request, which directly determines whether the summarization workload can meet its service-level objective. Option D is correct because output quality and token efficiency determine whether the summary is usable and how many tokens must be generated, and fewer generated tokens translate directly into lower end-to-end latency. Option B is not decisive on its own: parameter count correlates loosely with latency but does not guarantee it, since architecture, hardware, and serving optimizations also matter. Option C, the maximum input token limit, matters for handling long documents but does not address the strict latency constraint. Option E, fine-tuning availability, supports domain adaptation and accuracy rather than latency, so it is not one of the two most important factors here.

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Model size in billions of parameters.

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Maximum input token limit.

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Availability of fine-tuning capability for domain adaptation.

Question 12

Which THREE considerations are essential when deploying a generative AI application in a regulated industry such as healthcare?

Accepted Answer

Full audit trail of model inputs and outputs for accountability.. In a regulated industry such as healthcare, option B (full audit trail of model inputs and outputs) is essential because accountability and traceability are required to investigate incidents, demonstrate compliance during audits, and reconstruct how a clinical or patient-facing decision was produced. Option C (robust content filtering) is essential because generative models can hallucinate or emit harmful advice, and filtering/guardrails are needed to prevent unsafe or inaccurate outputs from reaching patients or clinicians. Option E (data privacy and HIPAA compliance) is essential because healthcare data is protected health information, so the deployment must enforce safeguards such as access controls, encryption, and Business Associate Agreements to avoid regulatory violations. Option A is not essential in this context because lowest possible inference latency is a performance optimization, not a regulatory or safety requirement, and accuracy, privacy, and auditability take precedence. Option D is not appropriate because maximum creative freedom increases variability and hallucination risk, which directly conflicts with the determinism, safety, and compliance needs of regulated healthcare use.

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Lowest possible inference latency for real-time responses.

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Maximum creative freedom for the model to generate diverse responses.

Question 13

A company deployed a question-answering system using Amazon Bedrock with a knowledge base (RAG). Users report that the model often hallucinates facts not in the knowledge base. What is the most effective way to reduce hallucinations?

Accepted Answer

Improve the relevance of retrieved documents by refining the retrieval strategy. Hallucinations in RAG systems often stem from the model receiving irrelevant or low-quality retrieved documents, which forces it to rely on its parametric knowledge rather than the provided context. By refining the retrieval strategy—such as improving embedding quality, adjusting chunk overlap, or using hybrid search—the system ensures the foundation model has the most relevant information to ground its answers, directly reducing the likelihood of fabricating facts.

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Reduce the maximum context length to limit model input

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Fine-tune the foundation model on a large general corpus

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Increase the chunk size of documents in the knowledge base

Question 14

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 15

A data scientist fine-tuned a large language model on Amazon SageMaker for financial report generation. The model produces responses that are too short and incomplete, often cutting off mid-sentence. What parameter should be adjusted first?

Accepted Answer

Increase the maximum token count. The max tokens parameter limits the length of generated responses. Increasing it allows the model to produce longer completions. Temperature, top_p, and model change affect quality or diversity, but not the length cap.

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Increase the temperature parameter

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Increase the top_p parameter

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Switch to a different foundation model