Amazon AIP-C01 Practice Exam Questions

Amazon AIP-C01 Sample Question Answers

Question # 1

A GenAI developer is building a RAG system that uses Amazon Bedrock Knowledge Bases. The system needs to process 50 textbooks that are stored in an Amazon S3 bucket. The textbooks are all an average of 500 pages long. The system needs to generate a knowledge base to answer domain-specific customer questions. During initial testing on a subset of documents, the GenAI developer notices that query responses contain irrelevant information and sometimes miss critical context from the source materials. The GenAI developer must ensure that the solution provides accurate responses with low latency and no hallucinations. Which solution will meet these requirements?

A. Configure fixed-size chunking with a 256-token chunk size. Implement metadata filtering based on document sections. Use an Amazon Titan Embeddings model to create vector representations that are optimized for semantic search. 
B. Use Amazon ElastiCache to implement semantic caching for common queries. Use Anthropic Claude Sonnet to reformulate user queries. Configure real-time model feedback loops to continuously improve response quality based on user interactions. 
C. Apply hierarchical chunking with both 200-token chunks and 1,000-token chunks. Implement hybrid search that combines vector and keyword search. Adjust the relevance score threshold to filter out low-confidence retrieval results. 
D. Use semantic chunking to automatically segment documents based on topic boundaries. Configure the knowledge base to use a single embedding model for all content types. Implement query expansion to reformulate user questions before retrieval. 

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Question # 2

A company uses an application to process customer support tickets. The company wants to integrate AI-powered sentiment analysis and auto-response generation into the application by using Amazon Bedrock. The company wants to prioritize urgent issues and reduce initial response times by 40% compared to manual responses. The solution must process 100 concurrent webhook requests with response times under 500 ms. The solution must maintain 99.9% availability across multiple AWS Regions and authenticate all incoming requests. The company must avoid any authentication failures. The company does not want to modify the existing application infrastructure, which includes several ticketing systems that use multiple webhook authentication methods. The solution must support scaling to handle occasional spikes up to 250,000 daily tickets during peak periods. Which solution will meet these requirements?

A. Use an Amazon API Gateway REST API with a Regional endpoint to receive webhook requests and invoke AWS Lambda functions. Configure Lambda authorizers to validate all the webhook authentication methods. Configure the Lambda functions to call Amazon Bedrock to perform sentiment analysis and generate responses. Store results in Amazon DynamoDB global tables to provide multi-Region availability. 
B. Create AWS Lambda function URLs for each ticketing system. Configure the function URLs with the NONE authentication type. Configure separate Lambda functions to verify webhook signatures by using Hash-based Message Authentication Code (HMAC) validation in the function code. Deploy the functions to multiple Regions and use AWS Global Accelerator to route traffic. Use Amazon Bedrock to perform sentiment analysis and generate responses. Return responses through webhook callbacks. 
C. Set up an Amazon SQS queue in each Region to receive webhook messages. Use the SQS queue to invoke AWS Lambda functions that call Amazon Comprehend to perform sentiment analysis and Amazon Lex to generate responses. Use Amazon EventBridge to retry message delivery to the application API. 
D. Deploy an AWS AppSync GraphQL API to multiple Regions. Configure API tokens to authenticate incoming requests. Create GraphQL mutation resolvers that publish events to Amazon EventBridge. Configure EventBridge rules to invoke AWS Lambda functions that use Amazon Bedrock to perform sentiment analysis and generate responses. Use Amazon CloudFront to reduce latency. 

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Question # 3

A financial services company is building a fraud detection system by using Amazon Bedrock. The system will monitor activity in multiple stock trading applications that run in the United States and Europe. The system must process 1,000 transactions every second with sub-500 ms response times. The system must also maintain high availability during connectivity disruptions. The company must ensure that data for European customers is processed only in AWS Regions that are based in Europe. Which solution will meet these requirements?

A. Configure AWS Lambda functions and Amazon EKS applications to use the InvokeModel API with a global inference profile. Deploy an automated failover system that uses Amazon Route 53 health checks. Create a dedicated European inference profile and enable geographic cross-Region inference for European applications. Use Amazon CloudWatch alarms to monitor utilization metrics. 
B. Configure all applications to use the InvokeModel API with provisioned throughput for an Anthropic Claude model in each Region separately. Set up a custom Application Load Balancer to distribute traffic based on Regional capacity and response times. Implement a Regional failover mechanism that uses Amazon EventBridge rules to handle connectivity disruptions. 
C. Configure all applications to use the InvokeModelWithResponseStream API with ondemand throughput. Deploy an Amazon API Gateway REST API with Regional endpoints in each location where the company operates to route requests to the closest Amazon Bedrock endpoint. Create separate IAM roles for applications that run in the United States and Europe. Grant the IAM roles Region-specific permissions. 
D. Configure applications that run in the United States to use provisioned throughput with the InvokeModel API. Configure European applications to use a Europe-specific geographic inference profile to ensure data sovereignty. Configure automatic scaling for provisioned capacity based on utilization metrics. Use Amazon EventBridge and AWS Lambda functions to implement cross-Region failover mechanisms. 

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Question # 4

A healthcare company is deploying an AI system that uses a foundation model (FM) to help clinicians make diagnostic decisions. The company’s ethics board requires the AI system to demonstrate fairness across patient demographic groups and comply with medical AI governance policies. During initial testing, the AI system provides recommendations without clear explanations or decision tracing. Clinicians are unable to review how the AI system produces diagnostic conclusions. The company needs to implement a solution that provides transparent reasoning for AI outputs, enables systematic fairness testing, and ensures policy compliance for responsible AI use in healthcare settings. The solution must balance comprehensive explainability with real-time performance requirements. The solution must support rapid iteration for bias testing across multiple demographic variables. The solution must integrate seamlessly with existing clinical workflows while maintaining strict data privacy controls. The solution must handle complex medical and regulatory terminology. Which solution will meet these requirements?

A. Use Amazon SageMaker Clarify to generate model explanations. Use Amazon Augmented AI (Amazon A2I) to implement human review workflows. Use AWS Config to enforce compliance policies across the AI system. 
B. Use Amazon Comprehend Medical to analyze medical terminology. Use Amazon Textract to process documents. Use AWS CloudFormation to standardize deployment configurations. 
C. Use Amazon Bedrock agent tracing to provide reasoning traces. Use Amazon Bedrock Prompt Management with A/B testing to perform fairness evaluations. Use Amazon Bedrock Guardrails to ensure policy compliance. 
D. Use Amazon CloudWatch to collect performance metrics. Use Amazon EventBridge to trigger compliance checks. Use AWS Lambda functions to generate custom explanation reports. 

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Question # 5

A global financial services company hosts a fraud-alert system that uses an Amazon Bedrock foundation model (FM) to generate explanations for suspicious transactions. The company processes regulated financial data across three geographic areas. The system must maintain consistent responsiveness globally, support multi-Region failover, and provide full observability for audit and compliance teams. Load testing shows that the FM’s total inference time cannot be reduced. The company cannot increase its inference costs, change the FM, modify token counts, or provision additional compute capacity. Users report that the UI performs slowly because it waits for the complete model response before it shows any content. The company must improve perceived responsiveness during peak periods, when the system can receive 10,000–15,000 concurrent requests. The solution must maintain multiRegion resiliency and full monitoring visibility. Which solution will meet these requirements?

A. Enable response streaming by using the InvokeModelWithResponseStream API so the frontend can display generated tokens as the tokens arrive. Collect metrics in Amazon CloudWatch and enable distributed tracing to monitor streaming latency and Regional performance. 
B. Deploy Regional Amazon Bedrock inference endpoints. Set up latency-based Amazon Route 53 routing. Cache partially processed explanations in a global Amazon DynamoDB table to serve responses more quickly during peak periods. 
C. Use a Lambda@Edge preprocessing layer to condense inputs during peak periods. Asynchronously call Amazon Bedrock while the system returns interim placeholder responses to customers. 
D. Deploy AWS Lambda functions to handle inference requests across multiple AWS Regions. Increase Lambda concurrency limits. Scale down Amazon CloudWatch Logs retention to reduce backend load during peak periods. 

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Question # 6

An ecommerce company is using an Anthropic Claude Sonnet model in Amazon Bedrock to generate product recommendations. An AWS Lambda function retrieves customer purchase data from Amazon DynamoDB, product reviews from Amazon S3, and customer profile information from Amazon RDS. Then the function sends the data directly to the Amazon Bedrock model through API calls. Recently, customers who have extensive purchase histories have begun to receive incomplete recommendations. Amazon CloudWatch logs for the Lambda function show execution timeouts. CloudWatch logs for Amazon Bedrock API calls show intermittent errors. The company reviews the logs and finds that some requests are failing with context-length-exceeded errors. Other requests finish but appear to ignore portions of the input data. The company wants the recommendation system to consider all customer data when the system generates recommendations. The company wants to use Amazon Bedrock Knowledge Bases to improve data organization and retrieval. Which combination of solutions will meet these requirements? (Select TWO.) 

A. Implement a chunking strategy that divides the customer data into smaller segments. Configure the model to process each segment separately. Invoke the model a final time to synthesize the individual responses into comprehensive recommendations. 
B. Modify the prompt structure to place the most critical information at the beginning and end of the context window. Implement token-counting logic to truncate less important data when the interaction approaches the model’s maximum context length. 
C. Replace Claude Sonnet with a model that has a larger context-window capacity. Increase the Lambda function timeout to accommodate longer processing times for larger inputs. 
D. Configure the recommendation system to use the Converse API. Modify the additionalModelRequestFields parameter to increase the maximum token limit beyond the model’s default context-window size. 
E. Implement RAG by using a knowledge base to index the customer data with vector embeddings. Retrieve only the most semantically relevant information for each recommendation request based on the current customer context. 

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Question # 7

A media company is building an AI-powered content moderation system by using Amazon Bedrock. The system first classifies text by using a small, low-latency model. Then the system escalates requests that have a confidence score below 0.65 to a larger, more expensive model. The system must respond in near real time for high-confidence results. The system must process low-confidence requests asynchronously. The system must scale to meet sudden spikes in demand. The company wants to optimize costs for the system by invoking the larger model only when required. The company wants to use decoupled components to achieve high resiliency for the system. Which solution will meet these requirements?

A. Use Amazon API Gateway to invoke the small model synchronously. If the small model’s confidence score is below 0.65, synchronously call the larger model. Use provisioned concurrency to handle traffic spikes. 
B. Use an AWS Step Functions workflow that has parallel branches to run both the small model and the large model for every request. Choose the large model result when confidence score values differ. 
C. Send requests to an Amazon SQS queue. Use AWS Fargate to process messages. Invoke the small model first. If the confidence score is below 0.65, place the request in a second SQS queue to process asynchronously by using the large model. 
D. Deploy both models on Amazon EC2 instances and enable auto scaling. Use a custom application heuristic to route requests to the appropriate instance based on phrase length and keyword rules. 

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Question # 8

A financial services company processes more than 10,000 customer inquiries every day through a multi-agent GenAI application that uses Amazon Bedrock AgentCore. The application agents invoke several custom tools. During peak usage periods, users report that the custom tools experience up to 40% failure rates. The tools perform inconsistently for different teams at the company. A GenAI developer must implement an observability solution that provides end-to-end visibility into agent interactions and tool behavior. The solution must use built-in Amazon Bedrock capabilities and must not require custom instrumentation. The GenAI developer needs a solution that requires minimal performance overhead. Which solution will meet these requirements?

A. Enable AgentCore Observability and trace collection. Use AWS X-Ray to capture distributed traces for the custom tools. Build Amazon CloudWatch dashboards to visualize metrics for errors, throttling, and latency during peak usage periods. 
B. Use Amazon CloudWatch Container Insights to monitor the agents. Configure an AWS Lambda function to poll the Amazon Bedrock API for tool usage metrics. Configure the function to store results in CloudWatch to generate alerts. 
C. Build a custom ETL pipeline that uses AWS Lambda functions to process Amazon CloudWatch logs from Amazon Bedrock. Store the processed data in Amazon DynamoDB. Use Amazon QuickSight to visualize cross-team performance patterns. 
D. Enable AgentCore Observability and send trace data to Amazon CloudWatch Logs. Use a custom AWS Lambda function to extract tool performance metrics from the logs. Use Amazon Managed Grafana to visualize trends. 

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Question # 9

A research company is developing a GenAI system to produce summaries of technical documents. The company must catalog all data sources in a central location. The company needs a solution that can automatically discover and update data sources. The solution must tag each generated summary with citations as metadata that users can query. The solution must retain tamper-evident, immutable audit logs for every model invocation and store input/output records. Which solution will meet these requirements?

A. Use Amazon Comprehend to identify data sources in the documents. Store generated summaries in Amazon S3 and enable S3 Object Lock. Use Amazon CloudWatch metrics to generate reports about application throughput. Do not include logs for each invocation. 
B. Use AWS Glue Data Catalog with crawlers to maintain data sources. Store generated summaries in Amazon S3. Write object tags that include a source ID. Store Amazon Bedrock model invocation logs in Amazon S3. Enable S3 Object Lock on the S3 bucket that stores invocation logs. Use AWS CloudTrail log file integrity validation to provide tamper-evident immutability. 
C. Store application outputs in Amazon DynamoDB. Apply item-level tags that include source attribution. Write application events to Amazon CloudWatch Logs. Use IAM roles to provide audit traceability. 
D. Use AWS AppConfig feature flags to implement data versioning. Restrict access to the model by using IAM condition keys. Maintain a versioned mapping file of source-to-output relationships in Amazon S3.

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Question # 10

A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling. A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company’s approved resources. The company does not want to make project-level modifications. Which solution will meet these requirements? 

A. Create a Git repository that contains all of the approved internal libraries, algorithms, and code samples. Include this Git repository in the application project locally as part of the workspace. Ensure that the developers use the workspace context to retrieve suggestions from the Git repository. 
B. In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder. 
C. Create a folder in the application project named rules. Store the guidelines and code in the folder for Amazon Q Developer to reference for code suggestions. 
D. Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application. 

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