AIP-C01 Practice Exam 3 — 20 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

Question 11

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.

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To improve model inference speed.

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Because the models cannot process numbers.

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To reduce the file size.

Question 12

Which AWS tool enables you to test different models side-by-side using the same prompt?

Accepted Answer

Bedrock Playground. Amazon Bedrock Playground provides a dedicated interface where you can select multiple foundation models (e.g., Anthropic Claude, AI21 Labs Jurassic, Amazon Titan) and run the same prompt against them simultaneously, comparing outputs side-by-side. This is a built-in feature of the Bedrock console designed specifically for model evaluation and prompt experimentation. It eliminates the need to write code or manage infrastructure, allowing quick assessment of model performance, latency, and response quality.

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Amazon SageMaker Studio

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AWS Cloud9

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AWS Lambda Console

Question 13

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.

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To provide a graphical user interface.

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To manage VPC networking.

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To store document metadata.

Question 14

You notice your SageMaker endpoint is underperforming. You identify that the model is CPU-bound during inference. Which action should you take?

Accepted Answer

Change to an instance type optimized for compute. If the model is CPU-bound during inference, the bottleneck is compute, not memory or instance count. Moving to a compute-optimized instance family (e.g., C-series) provides more vCPU per dollar and higher clock speeds, directly relieving the CPU saturation. This is the targeted fix for a CPU-bound workload.

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Reduce the batch size

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Increase memory limit in the container

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Increase the number of instances (horizontal scaling)

Question 15

Which THREE strategies should be implemented to ensure responsible AI practices in a production workload?

Accepted Answer

Perform regular model evaluation for bias. Option A is correct because regularly evaluating the model for bias (e.g., using fairness metrics such as demographic parity or equalized odds across subgroups) detects and mitigates discriminatory behavior before it harms users, which is a core responsible-AI control. Option C is correct because maintaining comprehensive logs of inputs, outputs, model versions, and decisions provides auditability and traceability, enabling incident investigation, compliance reporting, and accountability in production. Option D is correct because human-in-the-loop review for critical outputs ensures a qualified person validates high-impact decisions, catching harmful or erroneous model results that automation alone should not finalize. Option B does not belong because excluding all logging destroys auditability and incident forensics rather than protecting privacy; privacy should be handled via data minimization, encryption, and access controls. Option E does not belong because letting users train the model directly in production risks data poisoning, uncontrolled drift, and loss of governance over model behavior.

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Exclude all logging to protect privacy

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Allow users to train the model directly

Question 16

An application uses Amazon Bedrock InvokeModel to generate text. You notice that the output length is consistently truncated. Which parameter should you adjust to ensure the full response is captured?

Accepted Answer

Increase the max_tokens parameter. In Amazon Bedrock InvokeModel, the max_tokens parameter (or its model-specific equivalent like max_gen_len) controls the maximum number of tokens the model can generate in the response. If output is consistently truncated, increasing max_tokens allows the model to produce a longer completion. The other parameters affect randomness or stopping criteria, not the length cap.

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Increase the temperature parameter

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Increase the top_p parameter

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Decrease the stop_sequences parameter

Question 17

A company requires that every AI-generated document must include a watermark for traceability. How can this be handled in Bedrock?

Accepted Answer

Use a custom post-processing Lambda function to inject metadata. Bedrock does not natively support watermarking of generated text, so the correct approach is to intercept the model response and apply a custom post-processing step — typically an AWS Lambda function — that injects a watermark, metadata tag, or traceable identifier into the output. This gives the company full control over the watermark format and ensures every generated document carries the required traceability marker.

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Use fine-tuning to force the model to watermark

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Enable watermarking in the Bedrock console settings

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Use AWS KMS to sign the generated text

Question 18

An application using Amazon Bedrock is experiencing high latency. You need to monitor the 'ModelInvocationLatency' metric to identify performance bottlenecks. Which AWS service provides the primary integration for this telemetry?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the primary monitoring service for AWS services including Amazon Bedrock. Bedrock automatically publishes metrics such as ModelInvocationLatency, InvocationCount, and InvocationThrottles to CloudWatch under the AWS/Bedrock namespace. You can create alarms, dashboards, and custom metrics from this telemetry to identify performance bottlenecks.

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AWS CloudTrail

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AWS X-Ray

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Amazon OpenSearch Service

Question 19

When using the Bedrock Converse API, how do you provide multiple documents to the model for consideration?

Accepted Answer

Include them in the 'document' field within the messages array. The Bedrock Converse API allows you to include documents directly in the messages array using the 'document' field. This enables the model to consider multiple documents in a single request without needing a Knowledge Base or S3 URLs.

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Upload them to S3 and include the URL in the system message

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They must be indexed in a Knowledge Base first

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Attach them as multi-part form data in the HTTP request

Question 20

You are using Amazon Bedrock and need to ensure your application respects regional service quotas. Which service allows you to request a quota increase?

Accepted Answer

AWS Service Quotas. AWS Service Quotas is the centralized service for managing and requesting increases for AWS service limits.

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AWS IAM

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Amazon CloudWatch

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AWS Support Center