AIF-C01 Applications of Foundation Models Practice Test 11 — 15 Questions

Question 1

A developer is integrating an Amazon Bedrock foundation model into an application that must support multi-turn conversations, maintain chat history, and switch between different provider models with minimal code changes. The application should use a consistent request and response format. Which Amazon Bedrock API should the developer use?

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

Accepted Answer

The Converse API. The Converse API offers a unified conversational interface with a consistent message structure and response format across supported foundation models, simplifying multi-turn applications and reducing code changes when switching providers. InvokeModel requires provider-specific payloads, while model listing and customization job APIs serve discovery and training rather than conversational inference.

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The InvokeModel API

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The ListFoundationModels API

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The CreateModelCustomizationJob API

Question 2

A media company wants to automatically generate short video captions from uploaded audio files using a foundation model on AWS. The solution should transcribe speech and then produce concise captions. Which combination of AWS services should they use?

Accepted Answer

Amazon Transcribe to convert audio to text, then Amazon Bedrock to generate captions from the transcript.. The task requires two capabilities: speech-to-text and generative text creation. Amazon Transcribe provides accurate automatic speech recognition, and Amazon Bedrock supplies foundation models that turn the transcript into concise captions. Together they form a managed, scalable pipeline that meets the scenario without custom model development.

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Amazon Kinesis Data Streams to ingest audio, then Amazon Bedrock to generate captions directly from the audio stream.

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Amazon Rekognition to analyze the audio, then Amazon Bedrock to generate captions.

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Amazon Polly to convert audio to text, then Amazon Comprehend to generate captions.

Question 3

A developer is using Amazon Bedrock to generate summaries of news articles. They notice that the model sometimes includes information not present in the original article. Which term describes this phenomenon?

Accepted Answer

Hallucination. The phenomenon where a foundation model generates information not present in the source material is called hallucination. It is a key challenge in generative AI, especially for summarization tasks. Mitigation includes grounding the model with retrieval-augmented generation or using guardrails to filter unsupported claims.

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Data leakage

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Underfitting

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Overfitting

Question 4

A media company runs a daily news digest built on Amazon Bedrock with Anthropic Claude. Editors complain that summaries of long policy documents sometimes omit the final recommendations, even though the source text clearly contains them near the end. The requests currently pass only the document body and set a maximum output length of 300 tokens. Which change best addresses the truncation of the source content before the model reasons over it?

Accepted Answer

Chunk the document and apply retrieval or summarization so all sections reach the model.. The symptom points to input truncation: content beyond the context window is discarded before the model sees it, so the closing recommendations vanish. Increasing output length or changing sampling cannot recover text that never entered the prompt. Chunking the document, then summarizing or retrieving relevant chunks, ensures the full source is represented and the recommendations reach the model.

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Raise the temperature setting so the model explores more of the document.

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Increase the maxTokens parameter so the digest can be longer.

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Switch the model invocation to streaming responses.

Question 5

A healthcare company uses Amazon Bedrock with a foundation model to generate patient education materials. They must ensure that the model does not include protected health information (PHI) in its responses, even if it appears in the prompt. Which Amazon Bedrock feature should they configure?

Accepted Answer

Guardrails for Amazon Bedrock with sensitive information filters. Guardrails for Amazon Bedrock provides sensitive information filters that can detect and block or mask PII and custom patterns in both prompts and responses. Configuring these filters prevents PHI from appearing in generated materials. Model evaluation, provisioned throughput, and Agents do not offer runtime content redaction, so they cannot meet the compliance requirement.

Answer

Amazon Bedrock Agents with an action group to query a patient database

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Amazon Bedrock provisioned throughput

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Amazon Bedrock model evaluation with toxicity metrics

Question 6

A media company uses Amazon Bedrock to generate personalized news summaries for its subscribers. The model occasionally produces summaries that include outdated facts from its training data. The company wants the summaries to reflect only the most recent articles from its internal content management system (CMS). The CMS exposes a REST API that returns the latest articles. Which approach should the company take to ensure the generated summaries are grounded in the latest articles?

Accepted Answer

Use Amazon Bedrock agents with an action group that calls the CMS REST API to fetch the latest articles, and include those articles in the prompt.. The requirement is to ground generated summaries in the most recent articles from a CMS. Amazon Bedrock agents with action groups can call the CMS REST API at inference time, fetch the latest articles, and pass them as context to the foundation model. This retrieval-augmented approach ensures factual recency without retraining. Other options either alter generation parameters or involve static training methods that cannot dynamically incorporate fresh content.

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Fine-tune the foundation model using Amazon Bedrock continued pre-training with a small set of recent articles.

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Retrain the foundation model on the CMS articles daily using Amazon Bedrock custom model training.

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Increase the model's temperature setting to encourage more creative and current outputs.

Question 7

A startup wants to build a mobile app that generates personalized workout plans using a foundation model. They need to minimize infrastructure management and pay only for what they use. Which AWS service should they use to access foundation models via a single API?

Accepted Answer

Amazon Bedrock. The startup needs a managed service that offers foundation models via a single API with pay-as-you-go pricing. Amazon Bedrock provides exactly that, allowing developers to experiment with and deploy models from multiple providers without managing infrastructure. Other services like SageMaker require more management, while Comprehend and Polly serve different purposes.

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Amazon Polly

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Amazon Comprehend

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Amazon SageMaker

Question 8

A company is using Amazon Bedrock to build a conversational agent. They want to ensure the agent maintains context across multiple turns in a conversation. Which TWO strategies should the developer implement? (Choose two.)

Accepted Answer

Include the entire conversation history in each prompt sent to the model.. To maintain context across multiple turns, developers must include conversation history in each prompt. This can be done by sending the full history or by storing history externally and retrieving relevant parts. Foundation models are stateless, so context must be explicitly provided. Options suggesting built-in memory or session management are incorrect.

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Use Amazon Bedrock's session management feature to automatically persist conversation state.

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Enable the model's memory parameter to retain information across API calls.

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Use a single API call with a streaming response to keep the connection open and maintain context.

Question 9

A healthcare company is building an application on Amazon Bedrock that uses a foundation model to answer patient questions about medications. The company must reduce the risk of harmful or inaccurate medical advice. Which TWO strategies should they implement? (Choose two.)

Accepted Answer

Use Amazon Bedrock Guardrails to filter harmful content and define denied topics related to specific medical advice.. Guardrails provide a managed safety layer that filters harmful content and blocks denied medical topics, while RAG grounds responses in approved clinical guidelines so answers reflect authoritative sources. Together they reduce both harmful outputs and factual inaccuracies, which is essential for a patient-facing medication assistant.

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Disable all logging so patient interactions are not stored.

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Fine-tune the model on a small set of patient questions and answers collected from previous chats.

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Increase the model's temperature to make answers more diverse and comprehensive.

Question 10

A retail analytics team wants an assistant that answers questions about last quarter's sales using data stored in an Amazon S3 bucket of PDF reports and CSV exports. They want the model to cite the underlying documents and avoid inventing figures. Which Amazon Bedrock capability should they use?

Accepted Answer

Knowledge Bases for Amazon Bedrock with the S3 bucket as a data source.. Grounding answers in private documents requires retrieval, not filtering or capacity. Knowledge Bases for Amazon Bedrock manages ingestion, chunking, embedding, and retrieval from the S3 source, so responses are generated from the sales reports and can include citations. Evaluation, Guardrails, and Provisioned Throughput address quality measurement, content safety, and capacity respectively, none of which supply the source data.

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Guardrails for Amazon Bedrock configured with a denied topics policy.

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Model evaluation jobs that score responses against a ground-truth dataset.

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Provisioned Throughput purchased for the foundation model.

Question 11

A developer is building a prototype that needs to generate product descriptions from a few keywords. The developer wants to use a foundation model on Amazon Bedrock but has no labeled training data and wants to avoid managing infrastructure. Which approach should the developer use?

Accepted Answer

Use the foundation model with a well-crafted prompt that includes the keywords and desired format.. For a prototype with no labeled data and a desire to avoid infrastructure management, using a foundation model on Amazon Bedrock with a well-designed prompt is ideal. Prompt engineering allows the model to generate product descriptions from keywords without any training. Fine-tuning, training from scratch, or self-managing an EC2 deployment all introduce unnecessary complexity and data requirements that the scenario does not support.

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Deploy an open-source model on an Amazon EC2 instance and write custom inference code.

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Train a custom model from scratch using Amazon SageMaker.

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Fine-tune a foundation model with a small dataset of product descriptions.

Question 12

A company is deploying a foundation model on Amazon Bedrock to generate product descriptions. They want to ensure the model's output is factually consistent with the provided product specifications and avoids hallucinated features. Which TWO techniques should they use? (Choose two.)

Accepted Answer

Use retrieval-augmented generation (RAG) with Amazon Bedrock Knowledge Bases to ground the model in the product specifications.. To ensure factual consistency, the company should ground the model in the actual product specifications. Retrieval-augmented generation with Amazon Bedrock Knowledge Bases provides relevant context, while clear prompt instructions that restrict the model to given information further reduce hallucinations. Both techniques work together to keep output aligned with facts.

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Set the temperature to a high value to encourage the model to explore more creative descriptions.

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Fine-tune the model on a dataset of existing product descriptions to improve its writing style.

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Use Amazon Bedrock Guardrails to define a denied topic for features not present in the specifications.

Question 13

A retail company wants to compare the output quality of several foundation models available in Amazon Bedrock for a product description generation task. They need a repeatable, automated way to score responses against reference descriptions. Which AWS capability should they use?

Accepted Answer

Amazon Bedrock model evaluation jobs with automatic metrics such as BERTScore and ROUGE.. Amazon Bedrock model evaluation jobs run automated scoring against reference datasets using metrics such as BERTScore and ROUGE, enabling consistent comparison of multiple foundation models. This directly supports selecting the best model for product description generation with repeatable, quantitative results rather than subjective manual review.

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AWS Trusted Advisor to assess foundation model performance.

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Amazon Bedrock provisioned throughput to benchmark model latency.

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Amazon CloudWatch Logs to capture model responses and manually review them.

Question 14

A financial services company is building an application on Amazon Bedrock that generates personalized investment summaries. The compliance team requires that every generated summary includes an exact, verifiable citation from the company's approved regulatory documents, and that the model must not fabricate any citation. The company has a large corpus of approved PDF documents stored in Amazon S3. Which approach should the company use to meet these requirements?

Accepted Answer

Use Amazon Bedrock Knowledge Bases to ingest the S3 documents and configure the model to generate responses grounded in retrieved passages, then verify the returned source citations.. Grounding the model in an approved document corpus is the reliable way to produce verifiable citations. Amazon Bedrock Knowledge Bases ingests the S3 documents, retrieves relevant passages per query, and returns source attributions, so generated summaries reference real approved text instead of relying on parametric memory. Sampling settings, fine-tuning, and logging do not provide retrieval-backed citation integrity.

Answer

Enable model invocation logging in Amazon Bedrock and audit the logs to confirm that any citation the model produces exists in the S3 corpus.

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Increase the model's temperature setting so the model explores more of its training data and is more likely to recall the exact regulatory text.

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Fine-tune the foundation model on the approved regulatory documents using Amazon Bedrock custom models, then rely on the fine-tuned model to reproduce exact citations from memory.

Question 15

A financial services company is using Amazon Bedrock to generate investment summaries. They must ensure that the model does not provide personalized financial advice, which is a regulatory requirement. Which AWS feature should they use to block the model from generating such advice?

Accepted Answer

Amazon Bedrock Guardrails with a denied topics policy.. To block the model from generating personalized financial advice, the company should use Amazon Bedrock Guardrails with a denied topics policy. This feature allows defining topics that the model must avoid, effectively preventing non-compliant responses. Other options like model evaluation or fine-tuning do not provide real-time blocking.

Answer

Amazon Bedrock model evaluation with a custom metric for regulatory compliance.

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Amazon Bedrock Agents with an action group that checks for financial advice keywords.

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Amazon Bedrock Provisioned Throughput with a custom model that has been fine-tuned to avoid financial advice.