AIP-C01 Foundation Model Integration Data And Compliance Practice Test 5 — 15 Questions

Question 1

Which TWO of the following are valid ways to improve the relevance of retrieved documents in a RAG system?

Accepted Answer

Implement metadata filtering to narrow the search space.. Metadata filtering and choosing an optimal chunking strategy are key levers to improve retrieval accuracy.

Answer

Use a larger, more generic embedding model.

Answer

Increase the number of retrieved chunks to 1000.

Answer

Delete all indices and recreate them every hour.

Question 2

A security auditor requests a report on Bedrock usage. Which TWO of the following AWS services can be used to generate this report?

Accepted Answer

Amazon CloudWatch.. Amazon CloudWatch (C) is correct because Bedrock publishes runtime metrics and invocation logs to CloudWatch, allowing an auditor to build usage reports from model invocation counts, latency, and token metrics. AWS CloudTrail (D) is correct because it records Bedrock API activity such as InvokeModel and Converse calls as management/data events, which can be queried and exported for an audit report on who used Bedrock and when. AWS CodePipeline (A) is a CI/CD orchestration service and does not capture Bedrock usage data. AWS AppConfig (B) is for deploying and managing application configuration and feature flags, not for reporting service usage. AWS Trusted Advisor (E) provides best-practice checks and recommendations, not detailed Bedrock invocation reporting.

Answer

AWS CodePipeline.

Answer

AWS AppConfig.

Answer

AWS Trusted Advisor.

Question 3

Which TWO types of models are generally available in Amazon Bedrock?

Accepted Answer

Embedding models.. Amazon Bedrock provides foundation models (FMs) accessed via a unified API, and these FMs fall into categories such as embedding models (Option A) and text generation models (Option E). Embedding models in Bedrock, such as Amazon Titan Embeddings or Cohere Embed, convert text into vector representations used for semantic search, RAG, and clustering, which is why Option A is correct. Text generation models, such as Anthropic Claude, Amazon Titan Text, Meta Llama, and Mistral, produce natural-language output for chat, summarization, and content creation, which is why Option E is correct. Options B, C, and D are incorrect because network load balancer configurations, hardware drivers, and OS kernel images are infrastructure and systems-level artifacts, not model types offered by Amazon Bedrock.

Answer

Network load balancer configurations.

Answer

Hardware drivers.

Answer

OS kernel images.

Question 4

Which THREE items must be configured when setting up a new Knowledge Base in Amazon Bedrock?

Accepted Answer

Embedding model selection.. Embedding model, data source, and vector database are the three mandatory components for a Knowledge Base.

Answer

Model evaluation dataset.

Answer

External firewall rules.

Question 5

What is the primary benefit of using a 'System Prompt' in Bedrock?

Accepted Answer

To provide instructions that guide the model's behavior.. The system prompt sets the behavior and constraints for the model, ensuring consistent and safe interactions.

Answer

To bypass authentication.

Answer

To increase the model's speed.

Answer

To compress the input tokens.

Question 6

Which of the following is a recommended practice when working with Bedrock APIs?

Accepted Answer

Apply the principle of least privilege to IAM policies.. Following the principle of least privilege in IAM is the most recommended security practice.

Answer

Use the root user for API calls.

Answer

Disable logging to save costs.

Answer

Hardcode IAM keys in the code.

Question 7

When designing for compliance, which THREE actions are part of the 'Shared Responsibility Model' for an AWS customer?

Accepted Answer

Configuring encryption for the data at rest.. Data classification, encryption configuration, and IAM management are the customer's responsibility.

Answer

Maintaining the physical security of the data center.

Answer

Patching the underlying foundation model code.

Question 8

When preparing data for a Knowledge Base, why is it recommended to remove PII?

Accepted Answer

To comply with data privacy policies and reduce risk.. Removing PII ensures that the vector index does not store sensitive information, minimizing the risk of data leakage.

Answer

To improve model inference speed.

Answer

Because the models cannot process numbers.

Answer

To reduce the file size.

Question 9

A company is building a RAG application on Amazon Bedrock. They need to store embeddings for a large-scale knowledge base with low-latency similarity search and full integration with LangChain. Which vector database should the developer recommend?

Accepted Answer

Amazon OpenSearch Serverless vector engine. Amazon OpenSearch Serverless vector engine is purpose-built for storing and querying high-dimensional embeddings with k-NN similarity search, offering low-latency retrieval at scale and native LangChain integration via the OpenSearch vector store class. It removes cluster management overhead while supporting the vector workloads RAG applications require.

Answer

Amazon DynamoDB with global secondary indexes

Answer

Amazon Aurora PostgreSQL without pgvector

Answer

Amazon S3 with Athena query engine

Question 10

A healthcare company requires that all generative AI outputs be filtered for PII (Personally Identifiable Information) before being sent to the end user. Which Amazon Bedrock feature effectively manages this compliance requirement?

Accepted Answer

Amazon Bedrock Guardrails. Guardrails for Amazon Bedrock allows developers to configure PII filtering, profanity filters, and denial topics to ensure compliance.

Answer

AWS CloudTrail logs

Answer

AWS WAF on the API Gateway

Answer

IAM Policy evaluation for Bedrock

Question 11

A team is evaluating different models on Amazon Bedrock. They want to compare the performance of Claude 3 Sonnet versus Llama 3 on a specific prompt set without writing custom scripts. Which tool should they use?

Accepted Answer

Amazon Bedrock Prompt Management and Playgrounds. Bedrock Prompt Management and Playgrounds provide a GUI to test and compare models directly.

Answer

Amazon SageMaker Model Monitor

Answer

Amazon QuickSight

Answer

AWS Lambda functions

Question 12

A financial services firm is building a RAG application using Amazon Bedrock Knowledge Bases. They need to ensure that the vector embeddings for their proprietary documents are stored in a way that meets strict data residency requirements. The documents are stored in an Amazon S3 bucket in the eu-west-1 region. They want to use Amazon OpenSearch Serverless as the vector store. Which configuration should they choose?

Accepted Answer

Create the OpenSearch Serverless collection in eu-west-1 and configure the Bedrock Knowledge Base to use it.. Data residency requires that all data, including vector embeddings, remain within the specified region. By creating an OpenSearch Serverless collection in eu-west-1 and configuring the Bedrock Knowledge Base to use it, the embeddings are stored and processed within the EU. Other options either place data in a different region or use an alternative vector store that does not match the stated preference.

Answer

Create an OpenSearch Serverless collection in us-east-1 and use VPC peering to connect to the S3 bucket in eu-west-1.

Answer

Use the default OpenSearch Serverless collection provided by Bedrock Knowledge Bases, which automatically replicates data across regions.

Answer

Use Amazon Aurora PostgreSQL with pgvector as the vector store, deployed in eu-west-1, and configure the Knowledge Base to use it.

Question 13

A developer is building a generative AI application using Amazon Bedrock and needs to ensure that all API calls to the Bedrock runtime are logged for auditing purposes. The logs must include the request and response payloads, and must be retained for 90 days. The developer wants to minimize operational overhead. Which solution meets these requirements?

Accepted Answer

Enable model invocation logging in Amazon Bedrock, configure it to log to Amazon CloudWatch Logs, and set the retention period to 90 days.. Model invocation logging is a native Bedrock feature that can capture full request and response data and deliver it to CloudWatch Logs or S3. Configuring it to CloudWatch Logs with a 90-day retention period is the simplest way to meet the auditing requirement with minimal overhead. Other options either do not capture payloads or require custom code.

Answer

Create an AWS Lambda function that intercepts all Bedrock API calls, logs the payloads to Amazon S3, and applies a 90-day lifecycle policy.

Answer

Use AWS CloudTrail to log all Bedrock API calls, and configure an S3 bucket with a lifecycle policy to delete objects after 90 days.

Answer

Enable VPC Flow Logs for the VPC endpoint used by Bedrock, and store logs in CloudWatch with a 90-day retention period.

Question 14

A company is using Amazon Bedrock to generate product descriptions. They want to ensure that the model's responses do not include harmful or inappropriate content. They also need to monitor and log any blocked content for auditing. Which Bedrock feature should they use?

Accepted Answer

Guardrails for Amazon Bedrock. Guardrails for Amazon Bedrock provides configurable filters to detect and block harmful content across categories like hate, violence, and sexual content. It also supports logging of blocked content for auditing purposes. This makes it the appropriate feature to ensure responses are safe and to maintain an audit trail.

Answer

Model Invocation Logging

Answer

Provisioned Throughput

Answer

Bedrock Agents

Question 15

A developer is building a RAG application using Amazon Bedrock Knowledge Bases. The knowledge base is configured with an Amazon OpenSearch Serverless vector store. They notice that queries are returning irrelevant results. They suspect that the chunking strategy is not optimal for their documents, which are technical manuals with long paragraphs and code snippets. Which action should they take to improve retrieval accuracy?

Accepted Answer

Switch to a hierarchical chunking strategy that preserves document structure.. Hierarchical chunking preserves the structure of technical documents by creating parent and child chunks, allowing the retriever to match queries to precise child chunks while still providing the broader parent context. This is particularly effective for manuals with code snippets and long paragraphs, as it maintains semantic coherence and improves retrieval accuracy.

Answer

Reduce the number of retrieved chunks by lowering the top-k parameter.

Answer

Increase the chunk size to the maximum allowed by Bedrock Knowledge Bases.

Answer

Enable hybrid search by combining keyword and vector search.