Amazon AIP-C01 Questions Answers

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Amazon AIP-C01 Sample Questions

Question # 1

A healthcare company is using Amazon Bedrock to build a GenAI application to analyze patient feedback data from CSV files, JSON documents, and text files. The company needs to make the data available for a RAG solution that requires high data quality to prevent hallucinations. The GenAI application will use the data to make accurate clinical recommendations. The application must be highly scalable to handle data in near real time. Data attrition is also high. Before the company feeds data to a foundation model (FM), the company needs to validate data completeness, detect anomalies, remove personally identifiable information (PII), and monitor quality metrics. The application must be serverless, provide automated rule recommendations, and generate quality scores for regulatory compliance. Which solution will meet these requirements?

A. Use AWS Lambda functions to run custom validation logic. Store the results in an Amazon DynamoDB table. Use Amazon CloudWatch to generate and track quality scores. 
B. Use AWS Glue Data Quality to recommend and evaluate rules by using Data Quality Definition Language (DQDL), generate quality scores, and detect anomalies by using ML. Publish metrics to Amazon CloudWatch. 
C. Use Amazon SageMaker Data Wrangler to create transformation flows, apply quality checks, and export validated data to an Amazon S3 bucket. 
D. Use Amazon Comprehend to detect PII. Use AWS Lambda functions to validate data completeness. Store metrics in Amazon CloudWatch Logs. 

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Question # 2

A logistics company is using Amazon Bedrock to build an autonomous routing agent that coordinates with APIs that support warehouse, shipping, and international customs operations. The agent must meet the following requirements: • Break requests into reasoning steps. • Retry failed tool calls with backoff. • Stop retrying after three consecutive failures. • Require human approval for shipments that are valued over $100,000. • Use MCP to provide access to tools and new integrations without requiring code changes. Which combination of solutions will meet these requirements? (Select THREE.)

A. Use Amazon Bedrock AgentCore Gateway to convert the warehouse, shipping, and customs APIs into MCP-compatible tools. 
B. Use Task states in AWS Step Functions to orchestrate each reasoning step. Use retry configurations with exponential backoff to handle tool failures. 
C. Use a Choice state to route high-value shipments to a human approval workflow. 
D. Use Amazon Bedrock AgentCore with action groups for each API. Configure the agent's orchestration prompt to implement retry logic and human approval conditions. 
E. Use AWS Lambda functions that use MCP client libraries to invoke tools. Implement custom retry logic and circuit breaker patterns in Lambda function code. 
F. Use Amazon Bedrock Guardrails to block tool invocations for shipments that exceed the $100,000 threshold until a human approves the shipment through a separate workflow. 
G. Use Amazon API Gateway with AWS Lambda authorizers to validate tool requests and implement rate limiting. Implement custom retry logic with exponential backoff and a circuit breaker that halts retries after three consecutive failures. 

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Question # 3

A multinational bank wants to implement a RAG solution on AWS to run queries on internal policy and compliance documents. To comply with data residency regulations, the company must ensure that critical customer data remains within a specific AWS Region. The bank wants to use foundation models (FMs) on AWS to reduce infrastructure costs and minimize model maintenance. Which solution will meet these requirements?

A. Store critical customer data in a dedicated Amazon S3 bucket in the regulated Region. Configure Amazon Bedrock to directly access the documents through a VPC endpoint. Perform all RAG retrieval on AWS. 
B. Store critical customer data locally by using AWS Outposts in the regulated Region. Create embeddings locally in a secure vector store. Use Amazon Bedrock to orchestrate LLMs on AWS by using only retrieved context. 
C. Store critical customer data in a private Amazon OpenSearch Service cluster in a Region that is close to the regulated Region. Configure end-to-end encryption. Use an LLM on AWS to retrieve and summarize the data dynamically without storing embeddings locally. 
D. Containerize FMs on Amazon ECS in the regulated Region. Ingest critical customer data into the containerized FMs. Perform RAG queries entirely inside the container. 

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Question # 4

A financial services company is deploying a GenAI assistant that uses Amazon Bedrock to answer customer questions about account activity and company policies. The company must comply with responsible AI principles and governance requirements. The solution must meet the following requirements: • Prevent harmful, biased, or non-compliant responses. • Ensure consistent policy enforcement across all model invocations. • Provide traceability and auditability for AI-generated outputs. • Maintain developer productivity without embedding complex safety logic in application code. Which solution will meet these requirements?

A. Use Amazon Bedrock Guardrails to enforce content policies. Configure response filtering and topic restrictions. Configure logging for model interactions. 
B. Use prompt engineering techniques and system prompts to guide the model's behavior. 
C. Implement post-processing checks in AWS Lambda functions to manually review and redact model outputs before returning responses to users. 
D. Log all prompts and responses to Amazon S3. Conduct periodic human reviews to identify violations of responsible AI principles. 

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Question # 5

A company is using Amazon Bedrock to build a GenAI assistant that answers employee questions based on internal documentation. The company stores documents in Amazon S3, Atlassian Confluence, and an internal wiki system. The GenAI assistant must retrieve relevant content and provide grounded responses. The solution must meet the following requirements: • Integrate multiple document sources into a single retrieval layer. • Support semantic search rather than keyword-only queries. • Minimize custom ingestion and synchronization logic. • Ensure that retrieved content can be directly used to augment the GenAI assistant's foundation model (FM). Which solution will meet these requirements?

A. Use Amazon Bedrock Knowledge Bases and managed data connectors to ingest content from the source documents. Enable semantic retrieval to augment the FM. 
B. Index documents from the source documents into Amazon OpenSearch Service by using keyword mappings. Invoke the FM and manually select search results. 
C. Store the source documents in Amazon S3. Use AWS Lambda functions to generate embeddings. Implement custom retrieval logic in the GenAI assistant application layer. 
D. Store the source documents in Amazon DynamoDB. Query the DynamoDB table directly to provide contextual input to the FM. 

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Question # 6

A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company's data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3. The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application's decision-making processes and provide data classification capabilities. Which solution will meet these requirements?

A. Deploy the application in each Region with local IAM policies. Use Amazon Bedrock cross-Region inference to distribute the workload. Use Amazon CloudWatch to log AI decision-making processes and data processing activities. Manually track compliance certifications across Regions. 
B. Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit the decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region. 
C. Use Amazon S3 Object Lock with Region-specific S3 bucket policies. Pre-process the data points within the Region based on geographic origin before sending the data points to Amazon Bedrock. Use Amazon Macie to classify the data. Use AWS CloudTrail immutable logs to audit the decision-making processes. 
D. Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring to track model performance and compliance with data residency requirements. Create manual reports for each regulatory jurisdiction. 

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Question # 7

A company is building a meeting analysis solution for its executive team. The solution uses AWS generative AI services. The solution must extract speaker-attributed content from recorded meetings, analyze visual elements from presentation slides, and create searchable summaries that link speaker comments to relevant visual context. The solution must process 200 hours of meeting recordings each week. The solution must maintain data privacy by processing all meeting data within the AWS Cloud. The solution must store the source data for future retrieval and must be able to perform full-text searches. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Transcribe speaker diarization to process audio from the meeting recordings and to create speaker-attributed transcripts. Send video frames to Amazon Rekognition to perform image analysis. Use an AWS Lambda function to process outputs from Amazon Transcribe and Amazon Rekognition to generate searchable summaries that are stored in Amazon OpenSearch Service. 
B. Use Anthropic Claude Sonnet in Amazon Bedrock to process the meeting recordings by using multimodal capabilities to analyze both audio transcripts and video frames. Use Amazon Transcribe to identify speakers in meeting recordings. Store the linked data in Amazon OpenSearch Service. 
C. Use Amazon Bedrock to process meeting recordings. Use the Bedrock Data Automation (BDA) feature to extract audio streams. Define a custom output for the audio stream. Use Amazon Transcribe speaker diarization to transcribe recordings and identify speakers. Use Amazon Rekognition to analyze video frames. Store the output in Amazon DynamoDB. Use Amazon Bedrock to generate summaries that link speakers to visual elements. 
D. Use Amazon Transcribe to extract speaker-attributed content from meeting recordings. Use Anthropic Claude Sonnet in Amazon Bedrock to process the transcripts and video frames. Store the synchronized results in Amazon DynamoDB. Use a custom indexing scheme to enable rapid retrieval. 

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Question # 8

A company is building a custom agentic application. The company must have fine-grained control over the agent orchestration loop. The application must implement custom logic to select tools, handle multi-turn conversations that involve complex state management, integrate with proprietary logging systems, and implement custom retry strategies for tool failures. The company wants to use Amazon Bedrock FMs but must have full control over the orchestration logic. The company has expertise in building orchestration logic but wants to use AWS infrastructure to manage model inference and tool execution. Which solution will meet these requirements?

A. Use Amazon Bedrock AgentCore Runtime to deploy the application agents. Use AgentCore Gateway to integrate the application with tools. Use AgentCore Memory to manage conversation states. Use AgentCore Policy to handle tool selection and retry logic. 
B. Use Amazon Bedrock AgentCore to build a custom orchestration layer that controls the agent orchestration loop, tool invocation, and state management. Use Amazon Bedrock to manage model inference. 
C. Use Amazon Bedrock AgentCore built-in memory and session management capabilities to persist conversation state. Configure the managed tool execution runtime to automatically handle tool failures and retries. 
D. Use AWS Step Functions to orchestrate multiple calls to Amazon Bedrock AgentCore Runtime endpoints. Implement custom state management and retry logic between individual agent invocations. 

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Question # 9

A company provides a GenAI application that uses Amazon Bedrock to customers. The application accepts untrusted user inputs. The company observes that some users attempt to bypass system instructions by using prompt injection and jailbreak techniques. The company needs a solution to protect the application from malicious actors. The solution must meet the following requirements: • Detect and mitigate adversarial user inputs before the application invokes the model. • Enforce consistent safety controls during model inference. • Prevent the application from returning unsafe or manipulated outputs to users. • Use managed AWS services where possible to minimize the need for custom security logic. Which solution will meet these requirements?

A. Use AWS Lambda functions to sanitize user inputs. Apply Amazon Bedrock Guardrails during model inference. Validate outputs before returning responses. 
B. Use carefully engineered system prompts to discourage prompt injection and jailbreak attempts during model interactions.
 C. Restrict access to the application by using IAM authentication. Assume authenticated users will not submit adversarial inputs. 
D. Log all prompts and responses to Amazon S3. Query the prompts and responses periodically to identify adversarial behavior. 

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Question # 10

An insurance company is using Amazon Bedrock to build a claims processing application. The application must perform the following steps in sequence: analyze documents, extract data, and generate recommendations. Claims over $10,000 require an additional fraud analysis step before the application provides a recommendation. Which solution will meet these requirements with the LEAST operational complexity? 

A. Configure an AWS Step Functions workflow that uses Task states to handle each Amazon Bedrock invocation. Configure a Choice state to route claims based on the claim amount. 
B. Use Amazon Bedrock AgentCore to implement action groups to handle each step. Use agent reasoning to run conditional logic. 
C. Use Amazon Bedrock Prompt Flows to implement prompt nodes to handle each step. Use a condition node to route claims based on the claim amount. 
D. Configure AWS Lambda functions to invoke Amazon Bedrock to perform each step sequentially. Include conditional routing in the function code. 

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Question # 11

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 # 12

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 # 13

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 # 14

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 # 15

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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Amazon AIP-C01 Reviews