AIP-C01 Practice Test 4 — 10 Questions

Question 1

A company is using Amazon Bedrock to build a customer support chatbot and needs to ensure that PII is masked before the request reaches the foundation model. Which feature should the developer implement?

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

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the native feature designed to apply safeguards — including PII detection and masking — to prompts and responses before they reach or return from the foundation model. It lets you define sensitive information filters that identify PII (names, emails, SSNs, credit cards) and either block or mask it, satisfying the requirement that PII never reaches the model.

Answer

AWS KMS with envelope encryption

Answer

Amazon Macie

Answer

AWS WAF with custom regex rules

Question 2

When using Bedrock Converse API, how do you handle streaming responses?

Accepted Answer

ConverseStream. The Bedrock Converse API provides a dedicated streaming operation called ConverseStream, which returns a stream of events (e.g., messageStart, contentBlockDelta, messageStop) as the model generates tokens. This is the correct way to handle streaming with the Converse API because it is purpose-built for the unified conversational interface, supporting multi-turn chat and tool use. Unlike InvokeModelWithResponseStream (used with the InvokeModel API), ConverseStream maintains the Converse API's message format and event structure.

Answer

InvokeModelStream

Answer

Poll the API until finished

Answer

Request a callback URL

Question 3

A developer needs to share a Bedrock prompt template with another team securely. What is the recommended method?

Accepted Answer

Use Amazon Bedrock Prompt Management. Amazon Bedrock Prompt Management provides a secure, centralized way to create, version, and share prompt templates with other teams. It integrates with IAM for access control, allowing you to grant specific permissions to other teams without exposing the prompt publicly. This is the recommended method for secure sharing.

Answer

Save the prompt as a text file in S3 with public access

Answer

Send the prompt in an email

Answer

Store the prompt in a Git repository without encryption

Question 4

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

Answer

Cross-region inference

Answer

Model Invocation Logs

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

An organization is deploying a RAG architecture using Amazon Bedrock and Amazon OpenSearch Serverless. The security team requires that all data remain within the VPC. How should the developer configure the integration?

Accepted Answer

Deploy the Bedrock Knowledge Base using a PrivateLink interface endpoint for Bedrock and a VPC endpoint for OpenSearch Serverless.. To keep all data within the VPC, you must use AWS PrivateLink interface endpoints for Bedrock and a VPC endpoint for OpenSearch Serverless. This ensures that traffic between the Knowledge Base, Bedrock, and the vector store never traverses the public internet. The S3 Gateway Endpoint alone is insufficient because Bedrock and OpenSearch Serverless require interface endpoints.

Answer

Configure the Knowledge Base to use an S3 Gateway Endpoint only.

Answer

Use a NAT Gateway to route all traffic to the Bedrock service.

Answer

Use an API Gateway with a WAF to restrict access to the vector store.

Question 7

Which TWO metrics are natively available in Amazon CloudWatch for monitoring Bedrock model invocation performance?

Accepted Answer

InvocationLatency. Option B (InvocationLatency) is correct because Amazon Bedrock natively publishes the InvocationLatency metric to CloudWatch, which measures the time taken for a model invocation request to complete, directly reflecting model invocation performance. Option D (InvocationClientErrors) is correct because Bedrock also natively emits the InvocationClientErrors metric to CloudWatch, capturing the count of client-side errors (such as throttling or validation failures) during model invocations, which is essential for monitoring invocation health. Option A (VectorEmbeddingAccuracy) is not a CloudWatch metric provided by Bedrock; accuracy evaluation is handled through Bedrock model evaluation jobs, not runtime CloudWatch metrics. Option C (GPUUtilization) is not natively exposed by Bedrock in CloudWatch, since Bedrock is a fully managed service and does not surface underlying GPU infrastructure metrics to customers. Option E (ModelTrainingLoss) is not applicable because Bedrock does not expose training loss metrics in CloudWatch; Bedrock focuses on inference of foundation models rather than customer-visible training telemetry.

Answer

VectorEmbeddingAccuracy

Answer

GPUUtilization

Answer

ModelTrainingLoss

Question 8

Which TWO steps are required to properly secure Bedrock Knowledge Bases?

Accepted Answer

Use IAM roles for the Knowledge Base to access data. Enabling encryption and managing IAM access are the primary security configurations.

Answer

Disable all encryption to increase performance

Answer

Place the Knowledge Base in a public subnet

Answer

Grant public read access to the S3 bucket

Question 9

In the context of Bedrock Agents, what is the purpose of the 'Instruction' field in the agent configuration?

Accepted Answer

It serves as the system prompt to define the agent's behavior. The instruction field serves as the system prompt that defines the agent's persona and objective.

Answer

It defines the Knowledge Base search parameters

Answer

It stores user credentials for the API

Answer

It contains the list of API definitions

Question 10

Which THREE factors influence the choice of embedding model for a RAG solution?

Accepted Answer

Supported languages. Language support, embedding dimensions, and maximum input token length are key factors.

Answer

Model's popularity

Answer

Model training cost