AIP-C01 Foundation Model Integration Data And Compliance Practice Test 3 — 15 Questions

Question 1

A company is building a RAG application using Amazon Bedrock and needs to ensure that the data ingested into the knowledge base is filtered based on user identity. Which approach should the developer use?

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

Accepted Answer

Use Amazon Bedrock Knowledge Base metadata filtering during the retrieval phase to apply access controls.. Amazon Bedrock Knowledge Bases support metadata filtering during retrieval, allowing the application to pass user identity attributes as filters so only documents the user is authorized to see are retrieved. This enforces access control at the retrieval phase, before the model generates a response. It is the intended mechanism for identity-based filtering in RAG.

Answer

Use Lambda authorizers to filter the retrieved results after they are returned by the model.

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Create separate vector indexes for every user role within the Knowledge Base configuration.

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Implement fine-grained access control using IAM policies on the S3 bucket containing the source data.

Question 2

A company is building a RAG application using Amazon Bedrock and needs to ensure that the data ingested into the knowledge base is filtered based on user identity. Which approach should the developer use?

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

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails allows users to configure content filters, including PII redaction, to prevent sensitive information from being processed by or returned from foundation models.

Answer

AWS WAF on the Bedrock API endpoint

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Amazon Macie on the S3 bucket housing the source data

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AWS Glue DataBrew

Question 3

Which Amazon Bedrock feature allows developers to restrict user queries based on specific topics or harmful content categories?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails provide a structured way to define policies that filter input and output based on topics, PII, and harmful content.

Answer

Knowledge Base Vector Index policies

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Cross-region inference

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Model Invocation Logs

Question 4

When designing a RAG system, why would a developer choose Amazon Titan Text Embeddings over a third-party embedding model?

Accepted Answer

It is optimized for Amazon Bedrock Knowledge Bases, ensuring seamless integration and scalability.. Amazon Titan Embeddings are optimized to work natively with Amazon Bedrock Knowledge Bases and provide high performance and integration benefits.

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It supports multi-modal input processing natively.

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It is the only model that supports English.

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It prevents the model from hallucinating.

Question 5

Which AWS service is used to monitor and log the prompts sent to an Amazon Bedrock model for auditing purposes?

Accepted Answer

AWS CloudTrail. AWS CloudTrail logs all API calls made to Amazon Bedrock, providing the necessary audit trail for prompt usage.

Answer

AWS Config

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Amazon Macie

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Amazon CloudWatch Logs

Question 6

A company wants to provide users with citations for the AI-generated answers in their RAG system. How is this achieved in Amazon Bedrock?

Accepted Answer

Use the RetrieveAndGenerate API which includes source attribution.. The RetrieveAndGenerate API in Amazon Bedrock Knowledge Bases natively returns citations alongside generated responses. When you call RetrieveAndGenerate, the response includes a citations array that maps generated text segments back to the specific source chunks retrieved from the knowledge base, including document URIs and excerpts. This is a built-in capability, so no custom comparison logic or prompt engineering is needed.

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Manually write code to compare generation results with source documents.

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Configure the Prompt Template to force the model to output document IDs.

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Enable the 'Citations' feature in the Model Evaluation console.

Question 7

Which data format is best suited for ingestion into an Amazon Bedrock Knowledge Base?

Accepted Answer

PDF, Markdown, or HTML files stored in Amazon S3.. Amazon Bedrock Knowledge Bases are designed to ingest unstructured text data from documents such as PDF, Markdown, and HTML files stored in Amazon S3. These formats are supported for parsing and chunking into vector embeddings. Storing them in S3 provides a scalable, durable source for ingestion.

Answer

Raw binary executable files.

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Encrypted raw stream data.

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Proprietary database dump files.

Question 8

How can a developer ensure that the retrieval step in a RAG application focuses only on the most relevant documents when the knowledge base contains millions of files?

Accepted Answer

Implement metadata filtering in the query.. Metadata filtering (A) allows the retrieval step to narrow down the search space by applying filters on document attributes (e.g., date, category, author) before performing vector similarity search. This ensures that only documents matching the filter criteria are considered, improving relevance and reducing noise when the knowledge base is large.

Answer

Increase the vector dimension size.

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Use a larger model for embedding.

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Reduce the chunk size.

Question 9

An organization wants to use a model that supports both text and image input. Which family of foundation models should they select in Amazon Bedrock?

Accepted Answer

Claude 3 Sonnet. Amazon Bedrock supports multi-modal models like Claude 3 Sonnet and Claude 3 Opus which handle both text and images.

Answer

Amazon Titan Text Express

Answer

Jurassic-2 Mid

Answer

Cohere Command R

Question 10

A company is concerned about model provider policy compliance. Where can they find the specific data processing terms for their chosen model?

Accepted Answer

In the AWS Service Terms and individual model provider documentation.. The AWS Service Terms and the Bedrock documentation page specify the data handling and privacy commitments for each model provider.

Answer

In the Amazon Bedrock console 'Model Evaluation' tab.

Answer

By asking the model during an inference session.

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In the Amazon CloudWatch monitoring dashboard.

Question 11

What is the primary role of an Embedding Model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors for semantic similarity search.. Embedding models convert text into numerical vectors that represent the semantic meaning of the content for vector search.

Answer

To act as a firewall for the RAG system.

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To compress large files for storage.

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To generate human-readable summaries.

Question 12

When migrating an on-premises RAG system to Amazon Bedrock, how should the team handle existing vector embeddings?

Accepted Answer

Re-embed the documents using a model supported by Amazon Bedrock.. When migrating a RAG system to Amazon Bedrock, existing vector embeddings from an on-premises system cannot be directly used because they were generated by a different embedding model. Embeddings are model-specific; dimensions and semantic spaces differ. Therefore, the documents must be re-embedded using an embedding model supported by Amazon Bedrock (e.g., Amazon Titan Embeddings or Cohere Embed). This ensures compatibility with Bedrock's vector store and retrieval.

Answer

Map the old embeddings to new dimensions using a linear function.

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Use the Bedrock Import Vector API.

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Import existing vectors directly into the OpenSearch index.

Question 13

A developer needs to measure the quality of a RAG application's responses. Which tool within Amazon Bedrock facilitates this?

Accepted Answer

Amazon Bedrock Model Evaluation.. Amazon Bedrock Model Evaluation allows users to evaluate their models or RAG pipelines against custom or built-in datasets.

Answer

Amazon CloudWatch Metrics.

Answer

AWS Glue Quality Rules.

Answer

Amazon SageMaker Model Monitor.

Question 14

A developer wants to ensure that a generative AI application does not provide medical advice. How should they configure this?

Accepted Answer

Create a Guardrail with a 'Denied Topics' policy for medical advice.. Amazon Bedrock Guardrails allow you to define a 'Denied Topics' policy that explicitly blocks the model from responding to user queries about specific topics, such as medical advice. This is a managed, configurable safeguard that works across models and is enforced at the API level, ensuring consistent behavior. Unlike prompt-based instructions, Guardrails provide a deterministic, policy-driven control that cannot be overridden by user input or model drift.

Answer

Disable the model's knowledge of medical terms.

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Instruct the model via the system prompt only.

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Use a prompt chain to filter out medical terms.

Question 15

A developer needs to handle documents that are too large to fit in a single prompt. What is the most recommended approach?

Accepted Answer

Use a Knowledge Base to perform retrieval and generation.. Knowledge Bases for Amazon Bedrock implement a RAG (Retrieval-Augmented Generation) pattern: documents are chunked, embedded, and stored in a vector store, then only the most relevant chunks are retrieved and injected into the prompt at query time. This lets the model answer questions over documents of effectively unlimited size without exceeding the model's context window. It is the AWS-recommended, scalable approach for large-document Q&A.

Answer

Compress the text using a ZIP algorithm before sending.

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Concatenate all text and send it in one prompt.

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Ask the user to split the document.