AIP-C01 Practice Test 2 — 30 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

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Amazon Macie

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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

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Poll the API until finished

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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

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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

Question 11

Your organization wants to evaluate the quality and safety of model outputs using a formal testing framework. Which service is designed for this?

Accepted Answer

Amazon Bedrock Model Evaluation. Amazon Bedrock Model Evaluation is purpose-built to assess foundation model outputs for quality, accuracy, robustness, and safety (including toxicity and bias) using either automatic metrics or human/LLM-as-judge workflows. It is the only service in the list designed specifically for evaluating generative AI model outputs.

Answer

AWS CloudWatch Synthetics

Answer

Amazon SageMaker Pipelines

Answer

AWS CodePipeline

Question 12

A developer wants to prevent the Generative AI application from producing content related to hate speech or violence. What is the most efficient configuration in Amazon Bedrock?

Accepted Answer

Implement Amazon Bedrock Guardrails with content filters. Amazon Bedrock Guardrails provides configurable content filters that can block hate speech, violence, sexual content, insults, and misconduct, with adjustable strength levels per category. It is the most efficient configuration because it is a managed, no-code policy layer applied at inference time without retraining or modifying the model. Guardrails also supports denied topics, word filters, and PII redaction.

Answer

Create a custom fine-tuned model

Answer

Use AWS Config rules to monitor the model

Answer

Add a system prompt to the application code

Question 13

What is the role of an AWS Service Control Policy (SCP) in an AI governance framework?

Accepted Answer

To enforce organizational compliance and restrict service access. SCPs provide centralized guardrails to define the maximum permissions that can be granted to IAM roles across all accounts in an organization.

Answer

To store model weights securely

Answer

To automatically optimize model latency

Answer

To monitor user chat history

Question 14

In SageMaker JumpStart, what is the primary advantage of selecting a 'Provisioned Throughput' option for a deployed model?

Accepted Answer

It ensures dedicated capacity for the endpoint. Provisioned Throughput on a SageMaker JumpStart endpoint reserves dedicated inference capacity measured in Model Units, guaranteeing consistent latency and throughput regardless of other customers' traffic. It decouples your workload from shared on-demand capacity, which is critical for production workloads with steady or predictable traffic. This is the primary advantage over the default on-demand endpoint mode.

Answer

It automates the model fine-tuning process

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It reduces the size of the model weights

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It allows the model to access the internet

Question 15

You are implementing a Bedrock Guardrail. You need to ensure the model filters out specific PII (Personally Identifiable Information). Which capability should be configured?

Accepted Answer

Sensitive Information Filter. The Sensitive Information Filter in Bedrock Guardrails is specifically designed to detect and block or mask PII such as names, addresses, phone numbers, and other sensitive data. It uses pattern matching and machine learning to identify entities like SSNs, credit card numbers, and custom regex patterns. This directly addresses the requirement to filter out specific PII, unlike other guardrail components that focus on harmful content or topic restrictions.

Answer

Content Filter

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Denied Topics

Answer

Word Policy

Question 16

You are deploying a model using SageMaker Multi-Model Endpoints (MME). What is the primary benefit of this strategy for operational efficiency?

Accepted Answer

Reducing the number of instances required. MME allows you to host multiple models on a single set of instances, reducing the total cost by sharing resources.

Answer

Increasing model accuracy

Answer

Lowering training time

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Enabling faster model deployment

Question 17

Which AWS service is used to centralize and visualize costs for your various SageMaker projects?

Accepted Answer

AWS Cost Explorer. AWS Cost Explorer allows you to view, analyze, and visualize your AWS spending, including SageMaker usage.

Answer

CloudWatch Dashboards

Answer

SageMaker Studio Dashboard

Answer

AWS Trusted Advisor

Question 18

Which AWS tool is used to monitor the performance and latency of an application calling Bedrock?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch provides metrics for request latency, invocation count, and errors for Bedrock models.

Answer

Amazon Inspector

Answer

AWS CloudTrail

Answer

AWS Config

Question 19

What is the recommended approach to handle PII in your RAG Knowledge Base to comply with privacy regulations?

Accepted Answer

Redact PII before ingesting documents into the Knowledge Base. You should redact or mask PII at the source before ingestion into the Knowledge Base.

Answer

Filter it during query time only

Answer

Encrypt the Knowledge Base with a master key

Answer

Use a specialized model that doesn't 'read' PII

Question 20

Which TWO actions should you perform to identify cost savings in an existing SageMaker environment?

Accepted Answer

Review AWS Trusted Advisor recommendations. Analyzing Cost Explorer and using Trusted Advisor are the correct ways to find optimization opportunities.

Answer

Delete all data in S3

Answer

Increase the number of IAM roles

Answer

Deploy models to every region