Free Amazon AIP-C01 Exam Questions

Question 1

A financial services company uses an AI application to process financial documents by using AmazonBedrock. During business hours, the application handles approximately 10,000 requests each hour, whichrequires consistent throughput.The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. AmazonCloudWatch metrics show that the provisioned capacity is unused while on-demand requests are beingthrottled. The company finds the following code in the application:response = bedrock_runtime.invoke_model(modelId="anthropic.claude-v2",body=json.dumps(payload))The company needs the application to use the provisioned throughput and to resolve the throttling issues.Which solution will meet these requirements?

A : Increase the number of model units (MUs) in the provisioned throughput configuration.

B : Replace the model ID parameter with the ARN of the provisioned model that theCreateProvisionedModelThroughput API returns.

C : Add exponential backoff retry logic to handle throttling exceptions during peak hours.

D : Modify the application to use the invokeModelWithResponseStream API instead of the invokeModelAPI.

Answer: B

Question 2

An online gaming platform is launching a GenAI-powered support assistant using multiple Foundation Models (FMs) available in Amazon Bedrock (including Amazon Titan and Anthropic Claude). The security team has issued strict requirements for the assistant's input handling:PII Protection: Users must be prevented from submitting email addresses or phone numbers.Toxicity: Hate speech and insults must be blocked immediately.Custom Blocking: A specific list of known "cheat codes" and "exploit keywords" must be blocked from entering the model context.Consistency: These rules must apply uniformly across all used FMs without rewriting application logic for each model.Which solution should you implement to meet these requirements with the LEAST operational overhead?

A : Develop a pre-processing AWS Lambda function using the Python re library to detect PII patterns and cheat codes. Use Amazon Comprehend DetectSentiment API to identify hate speech. Configure the application to call this Lambda function and verify the result before calling InvokeModel on Amazon Bedrock.

B : Implement a standardized system prompt across all FMs that instructs the models to refuse to answer if the input contains emails, phone numbers, hate speech, or words from the cheat code list. Use the Amazon Bedrock Converse API to ensure the system prompt is strictly enforced by the model’s reasoning engine.

C : Use AWS WAF (Web Application Firewall) attached to the application load balancer handling the chat traffic. Create custom regex rules to block PII patterns and cheat codes. Enable the AWS Managed Rules for Anonymous IP List to reduce toxicity risks from bad actors.

D : Create an Amazon Bedrock Guardrail. Configure PII filters to block emails and phone numbers, and enable Content filters for hate speech. Use the Word filters feature to upload the list of cheat codes and exploit keywords. Associate the Guardrail with the InvokeModel API calls for all used FMs.

Answer: D

Question 3

A financial services company uses Amazon Bedrock to generate investment analysis reports. The prompt engineering team frequently updates prompts to improve output quality, but recent production incidents showed that prompt changes caused incorrect financial calculations.The company requires a solution that enables prompt versioning with the ability to rollback to previous versions, tracks which prompt version generated each report for audit compliance, sends alerts when prompt performance degrades (measured by downstream validation failures), and maintains a complete audit trail of all prompt changes for regulatory reviews.Which solution meets ALL these requirements with the LEAST operational overhead?

A : Use Bedrock Prompt Management to create and version prompts. Store each prompt version with metadata tags indicating the version number. Configure CloudWatch Logs to capture all Bedrock InvokeModel requests with prompt details. Create a Lambda function that parses CloudWatch Logs, detects validation failures, and publishes alerts to SNS. Store prompt change history in DynamoDB for audit reports.

B : Use Bedrock Prompt Management to create versioned prompts with immutable versions and aliases. Tag each report with the prompt version/alias used during generation and log this to CloudWatch Logs. Enable CloudTrail to capture all Bedrock API calls including prompt updates. Create CloudWatch metric filters on validation failure logs and configure CloudWatch Alarms to trigger SNS notifications when failure rates exceed thresholds. Use CloudTrail Event History and CloudWatch Logs Insights for audit trail queries.

C : Store prompts as text files in S3 with version control enabled. Implement a Lambda-based prompt versioning system that reads prompts from S3, tracks versions in DynamoDB, and passes prompts to Bedrock InvokeModel API. Configure S3 Event Notifications to trigger Lambda functions that log prompt changes to CloudWatch. Use CloudWatch Synthetics to test prompt quality and send alerts via SNS when tests fail.

D : Use AWS Systems Manager Parameter Store to manage prompts with versioning enabled. Create a Step Functions workflow that retrieves the prompt version from Parameter Store, invokes Bedrock, and logs the prompt version and output to CloudWatch Logs. Enable Parameter Store change notifications via EventBridge to track prompt updates. Create CloudWatch Alarms based on custom metrics published by the Step Functions workflow when validation failures occur.

Answer: B

Question 4

A marketing analytics company generates monthly reports that include AI-generated summaries of customer feedback. They have 10,000 customer feedback entries to summarize each month. The summaries are not time-sensitive and can be processed overnight. The team wants to optimize costs compared to processing requests one-by-one in real-time.Which processing approach should the developer use to reduce costs?

A : Invoke the foundation model in real-time for each customer feedback entry as soon as it arrives

B : Use provisioned throughput to guarantee capacity for all 10,000 requests

C : Use batch processing to send multiple requests together and process them during off-peak hours

D : Enable auto-scaling to handle the large burst of requests

Answer: C

Question 5

A company is designing a canary deployment strategy for a payment processing API. The system mustsupport automated gradual traffic shifting between multiple Amazon Bedrock models based on real-timeinference metrics, historical traffic patterns, and service health. The solution must be able to graduallyincrease traffic to new model versions. The system must increase traffic if metrics remain healthy anddecrease traffic if the performance degrades below acceptable thresholds.The company needs to comprehensively monitor inference latency and error rates during the deploymentphase. The company must also be able to halt deployments and revert to a previous model version without anymanual intervention.Which solution will meet these requirements?

A : Use Amazon Bedrock with provisioned throughput to host model versions. Configure an AmazonEventBridge rule to invoke an AWS Step Functions workflow when a new model version is released.Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWSLambda function to check Amazon CloudWatch performance metrics. Configure the workflow toincrease traffic if metrics meet thresholds and to trigger a traffic rollback if performance metrics fallbelow thresholds.

B : Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon APIGateway HTTP API with stage variables and weighted routing to shift traffic gradually. Use AmazonCloudWatch to monitor performance. Use external logic to adjust traffic and roll back if performancefalls below thresholds.

C : Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions.Use variant weights to shift traffic. Use Amazon CloudWatch and SageMaker Model Monitor to triggerrollbacks. Use EventBridge to roll back deployments if an anomaly is detected.

D : Use Amazon OpenSearch Service to track inference logs. Configure OpenSearch Service to invoke anAWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shifttraffic based on inference logs.

Answer: A