AIP-C01 Practice Test 5 — 10 Questions

Question 1

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.

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AWS CloudWatch Synthetics

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Amazon SageMaker Pipelines

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AWS CodePipeline

Question 2

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.

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Create a custom fine-tuned model

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Use AWS Config rules to monitor the model

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Add a system prompt to the application code

Question 3

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.

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To store model weights securely

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To automatically optimize model latency

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To monitor user chat history

Question 4

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.

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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 5

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.

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Content Filter

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Denied Topics

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Word Policy

Question 6

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.

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Increasing model accuracy

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Lowering training time

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Enabling faster model deployment

Question 7

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.

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CloudWatch Dashboards

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SageMaker Studio Dashboard

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AWS Trusted Advisor

Question 8

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.

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Amazon Inspector

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AWS CloudTrail

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AWS Config

Question 9

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.

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Filter it during query time only

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Encrypt the Knowledge Base with a master key

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

Question 10

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.

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Delete all data in S3

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Increase the number of IAM roles

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Deploy models to every region