AIP-C01 Foundation Model Integration Data And Compliance Practice Test 4 — 15 Questions

Question 1

What is the primary function of the Bedrock 'Model Invocation Logs'?

Accepted Answer

To capture and store prompt/response payloads for auditing.. These logs are used for auditing and debugging by capturing the request and response payloads.

Answer

To throttle incoming requests.

Answer

To perform real-time model retraining.

Answer

To reduce inference costs.

Question 2

A compliance team requires that all prompts containing sensitive data be encrypted. How does Amazon Bedrock handle this during the request?

Accepted Answer

Amazon Bedrock uses TLS 1.2+ for encryption in transit.. Amazon Bedrock encrypts all data in transit using TLS 1.2 or higher, including prompts sent to the API. This is automatic and does not require manual encryption or special encoding by the developer. Therefore, prompts containing sensitive data are encrypted in transit by default.

Answer

Bedrock only supports encrypted prompts if a VPC endpoint is used.

Answer

The prompt must be base64 encoded.

Answer

The developer must manually encrypt the prompt string before calling the API.

Question 3

Which API call in Amazon Bedrock is used to trigger a text generation request without a Knowledge Base?

Accepted Answer

InvokeModel. InvokeModel is the Bedrock runtime API used to send a direct inference request to a foundation model, passing a prompt in the model-specific request body and receiving generated text. It does not require a Knowledge Base because it operates directly against the model. RetrieveAndGenerate, by contrast, is specifically designed for RAG workflows that query a Knowledge Base.

Answer

RetrieveAndGenerate

Answer

GetModelInvocation

Answer

CreateKnowledgeBase

Question 4

When should a developer choose to fine-tune a model versus using RAG?

Accepted Answer

When you need the model to learn a specific, consistent corporate tone.. RAG is best for grounding in changing data, while fine-tuning is better for changing the model's style, tone, or highly specialized domain language.

Answer

When you have very little data.

Answer

When you need to ensure the model does not hallucinate.

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When the data changes daily.

Question 5

Which metric should a developer monitor to ensure the RAG system remains performant as usage increases?

Accepted Answer

Inference Latency.. Latency and Token Consumption (or request count) are key indicators of RAG performance and cost efficiency.

Answer

Vector Database Storage Capacity.

Answer

IAM User Count.

Answer

Model Training Loss.

Question 6

An organization is concerned about data residency requirements for their AI models. Where should they look to identify which AWS regions support specific models?

Accepted Answer

The Amazon Bedrock documentation region table.. The Amazon Bedrock documentation includes a region table that lists which foundation models are available in which AWS regions. This is the authoritative source for identifying model availability by region, which is essential for meeting data residency requirements. Organizations should consult this table before architecting their GenAI application to ensure the chosen model runs in an approved region.

Answer

The IAM console.

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The AWS CLI.

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The Amazon S3 bucket settings.

Question 7

When using a Knowledge Base, what is the 'Base Prompt' used for?

Accepted Answer

To instruct the model on how to format the answer using retrieved context.. The base prompt serves as the template for the LLM to understand how to use retrieved context to answer the user's question.

Answer

To define the database schema.

Answer

To authenticate the user.

Answer

To store the conversation history.

Question 8

Which service should be used to manage user access to Amazon Bedrock models?

Accepted Answer

AWS IAM.. AWS IAM (Identity and Access Management) is the service used to control access to AWS resources, including Bedrock APIs.

Answer

Amazon Route 53.

Answer

Amazon SageMaker.

Answer

AWS Glue.

Question 9

A developer wants to ensure that the RAG pipeline is resilient to outages. What architectural pattern should be used?

Accepted Answer

Multi-region deployment with cross-region replication for the vector database.. A multi-region deployment with cross-region replication of the vector database ensures that if one AWS region becomes unavailable, the RAG pipeline can fail over to a replica region that already holds the embedded document vectors, preserving retrieval and generation. This directly addresses resilience to outages by removing the single-region failure domain for both compute and the vector store. Cross-region replication keeps the vector index synchronized so failover does not require re-ingesting and re-embedding the corpus.

Answer

Single-node deployment.

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Hardcoding API endpoints.

Answer

Using only one S3 bucket.

Question 10

What is the purpose of 'Foundation Models' (FMs) in the context of Amazon Bedrock?

Accepted Answer

To perform generative tasks like text completion or analysis.. Foundation Models (FMs) in Amazon Bedrock are large pre-trained models (e.g., Anthropic Claude, Amazon Titan, Meta Llama) that perform generative tasks such as text completion, summarization, question answering, and analysis. They are the core AI engines that Bedrock exposes via a unified API, allowing you to build generative applications without training models from scratch. The purpose of FMs is to provide these generative capabilities as a managed service.

Answer

To provide a graphical user interface.

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To manage VPC networking.

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To store document metadata.

Question 11

A business requirement is to ensure that the RAG model output is grounded solely in the provided documents. How can this be enforced?

Accepted Answer

Set the model temperature to a very low value (e.g., 0.0) and use clear system instructions.. Setting temperature to a very low value (e.g., 0.0) makes the model's output more deterministic and reduces creative deviation, while clear system instructions explicitly tell the model to answer only from the provided documents. Together, these enforce grounding in the RAG context and minimize hallucination.

Answer

Increase the temperature of the model to 1.0.

Answer

Use a secondary model to re-check the output.

Answer

Disable the model's training data.

Question 12

A developer is configuring a RAG system and wants to ensure the model has access to the most recent data. How should they handle updates?

Accepted Answer

Perform a 'Sync' operation on the Knowledge Base.. The 'Sync' operation in Knowledge Bases should be triggered whenever the source data changes in S3 to update the index.

Answer

Wait for the Bedrock service to automatically detect changes.

Answer

Use a cron job to restart the inference engine.

Answer

Delete and recreate the knowledge base daily.

Question 13

Which TWO of the following are supported as source data for Amazon Bedrock Knowledge Bases?

Accepted Answer

Atlassian Confluence.. S3 is the primary supported source, and Confluence is also natively supported as a data source.

Answer

Email server logs.

Answer

Social media live streams.

Answer

Local hard drive files.

Question 14

Which THREE factors influence the cost of running an Amazon Bedrock Knowledge Base?

Accepted Answer

The amount of data stored in the vector database.. Option A is correct because an Amazon Bedrock Knowledge Base stores its embeddings in a vector store (for example, Amazon OpenSearch Serverless, Aurora pgvector, or Neptune Analytics), and the cost of that vector database scales with the volume of data/vectors stored. Option B is correct because the RetrieveAndGenerate API invokes a foundation model to generate the final answer, and you are billed for the input and output tokens the model consumes during that generation step. Option D is correct because ingesting documents into the knowledge base requires calling a Bedrock embedding model (such as Amazon Titan Text Embeddings), and you pay per token processed by that embedding model. Option C is not a Bedrock Knowledge Base cost factor because IAM users are free and concurrency of users does not drive Bedrock or vector-store charges. Option E is not correct because there is no per-scan charge for S3 buckets; S3 charges are based on storage, requests, and data transfer, not on how often the knowledge base syncs or scans the bucket.

Answer

The number of concurrent IAM users.

Answer

The frequency of S3 bucket scans.

Question 15

Which THREE actions can a developer take to mitigate hallucinations in a RAG application?

Accepted Answer

Use a clear system prompt that restricts the model to only use provided information.. Lowering temperature, providing context, and defining strict system prompts all help limit the model's creative deviation.

Answer

Use the retrieve-only approach and perform generation with a custom internal engine.

Answer

Allow the model to use its internal training data for any gap in knowledge.