Free Amazon AIP-C01 Exam Questions

Question 1

A content moderation system needs to identify specific grammatical patterns in user-generated text, such as detecting imperative sentences (commands) or questions. The team wants to understand the grammatical structure and parts of speech (nouns, verbs, adjectives) in text before making moderation decisions.Which Amazon Comprehend feature should they use?

A : Sentiment analysis

B : Syntax analysis (part-of-speech tagging)

C : Entity recognition

D : Key phrase extraction

Answer: B

Question 2

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 3

A company is building a multicloud generative AI (GenAI)-powered secret resolution application that usesAmazon Bedrock and Agent Squad. The application resolves secrets from multiple sources, including keystores and hardware security modules (HSMs). The application uses AWS Lambda functions to retrievesecrets from the sources. The application uses AWS AppConfig to implement dynamic feature gating. Theapplication supports secret chaining and detects secret drift. The application handles short-lived and expiringsecrets. The application also supports prompt flows for templated instructions. The application uses AWSStep Functions to orchestrate agents to resolve the secrets and to manage secret validation and drift detection.The company finds multiple issues during application testing. The application does not refresh expired secretsin time for agents to use. The application sends alerts for secret drift, but agents still use stale data. Promptflows within the application reuse outdated templates, which cause cascading failures. The company mustresolve the performance issues.Which solution will meet this requirement?

A : Use Step Functions Map states to run agent workflows in parallel. Pass updated secret metadata throughLambda function outputs. Use AWS AppConfig to version all prompt flows to gate and roll back faultytemplates.

B : Use Amazon Bedrock Agents only. Configure Amazon Bedrock guardrails to restrict prompt variation.Use an inline JSON schema for a single agent’s workflow definition to chain tool calls.

C : Use a centralized Amazon EventBridge pipeline to invoke each agent. Store intermediate prompts inAmazon DynamoDB. Resolve agent ordering by using TTL-based backoff and retries.

D : Use Amazon EventBridge Pipes to invoke resolvers based on Amazon CloudWatch log patterns. Storeresponse metadata in DynamoDB with TTL and versioned writes. Use Amazon Q Developer todynamically generate fallback prompts.

Answer: A

Question 4

A development team is building a serverless application where AWS Lambda functions need to invoke Amazon Bedrock foundation models to generate product descriptions. The security team requires that the Lambda functions follow the principle of least privilege when accessing AWS services.Which approach should the developer implement to grant the Lambda function access to Amazon Bedrock?

A : Embed AWS access keys and secret keys as environment variables in the Lambda function configuration

B : Grant the Lambda function's IAM role full Administrator access to ensure it can call all AWS services

C : Attach an IAM role to the Lambda function with the bedrock:InvokeModel permission for the specific models being used

D : Store IAM credentials in the Lambda function code and rotate them manually every 90 days

Answer: C

Question 5

A media streaming company currently runs a self-managed vector database on Amazon EC2 instances for their content recommendation system. The database stores 150 million video embeddings and handles approximately 50,000 similarity searches per day during peak hours, with search activity dropping to near zero during off-peak nights.The infrastructure team spends significant time managing database scaling, performing backups, applying security patches, and monitoring cluster health. Monthly costs include $8,000 for always-on EC2 instances plus operational overhead. The CTO wants to reduce infrastructure management burden and optimize costs for the variable workload pattern.Which migration strategy addresses these requirements MOST cost-effectively?

A : Deploy Amazon OpenSearch Serverless with vector engine capabilities. Configure auto-scaling policies to scale compute capacity down to minimum during off-peak hours while maintaining search availability.

B : Containerize the existing vector database using Amazon ECS on AWS Fargate. Implement scheduled scaling policies to stop tasks during zero-activity periods and restart them before peak traffic begins.

C : Migrate the vector database to Amazon RDS for PostgreSQL with pgvector extension. Enable Aurora Serverless v2 auto-scaling to reduce costs during low-traffic periods while maintaining managed database benefits.

D : Migrate embeddings to Amazon S3 vector buckets with vector indexes enabled. Configure S3 Lifecycle policies for index management and leverage the pay-per-request pricing model that eliminates costs during zero-activity periods.

Answer: D