Amazon AIP-C01 Questions Answers

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Amazon AIP-C01 Sample Questions

Question # 1

A transportation company is building a trip planning AI assistant that users access through a mobile app or a web portal. The AI assistant must interpret natural-language user requests that include departure times, accessibility needs, and transfer preferences. The AI assistant must query appropriate backend APIs to route requests, calculate fares, and retrieve information about travel disruptions. The company must ensure that the AI assistant generates all fare prices in a deterministic way. The AI assistant must never generate routes or fares on its own. The company must be able to audit all interactions for regulatory review. The company must be able to add new backend services in the future without re-training or modifying the model. Which solution will meet these requirements? 

A. Use an AWS Lambda function to directly invoke an Amazon Bedrock FM. Embed structured route planning and pricing rules in the prompts so the model will generate consistent outputs.
 B. Deploy Amazon Bedrock AgentCore with explicitly defined tools to enforce deterministic constraints. Enforce strict JSON I/O schemas. Ensure that the model can only explain tool outputs. 
C. Use RAG with Amazon OpenSearch Serverless to retrieve schedule and fare documents. Allow the model to synthesize route and cost options for users from the retrieved content. 
D. Use AWS Step Functions to coordinate multiple Amazon Bedrock model calls to improve output consistency without limiting the model's flexibility. 

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Question # 2

A GenAI developer is using Amazon Bedrock to build an application. The application needs to interpret complex user requests in natural language, maintain semantic context across multiple interactions with the same user, and dynamically invoke more than 50 enterprise APIs based on reasoning rather than predefined workflows. The application must autonomously coordinate backend tools and provide explainable results for compliance reviews. The GenAI developer wants to use Amazon Bedrock AgentCore to implement dynamic orchestration. Which solution will meet these requirements?

A. Use AgentCore with AWS Step Functions to define static workflows that call APIs. Configure Amazon CloudWatch Logs to collect logs for compliance reviews. 
B. Use AgentCore to integrate reasoning modules with dynamic tool invocation APIs. Configure AgentCore agents to orchestrate enterprise APIs based on semantic context and to log reasoning traces for compliance reviews. 
C. Use Amazon Bedrock Knowledge Bases to implement semantic search instead of enterprise APIs. Integrate AWS Lambda functions with an AgentCore agent to call APIs to orchestrate runtimes. 
D. Use Amazon Bedrock Guardrails with an external LLM chain to manage semantic context and to collect logs for compliance reviews. Invoke APIs directly from the application. 

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Question # 3

A financial services company deploys a RAG application. The application uses Amazon OpenSearch Service to provide vector storage and Amazon Bedrock to generate text. The application ingests thousands of documents daily and processes hundreds of user queries each hour. Several weeks after the deployment, users report increased response times despite sufficient compute resources. The company needs a monitoring solution that proactively identifies performance issues across the entire RAG pipeline. Which solution will meet this requirement?

A. Configure Amazon CloudWatch alarms for the OpenSearchDashboardsHealthyNodes metric and the SearchLatency metric. Manually investigate and restart OpenSearch Service instances whenever query response times exceed predefined thresholds based on historical performance patterns. 
B. Use Amazon CloudWatch Container Insights to monitor the OpenSearch Service cluster. Use Amazon EventBridge to invoke AWS Systems Manager Automation to restart the OpenSearch Service instances whenever memory usage exceeds 80%. 
C. Create basic Amazon CloudWatch dashboards to track CPU and memory usage for individual services. Configure alarms based on static thresholds for query latency. Set up email notifications when thresholds are exceeded. 
D. Use Amazon CloudWatch dashboards to monitor end-to-end RAG metrics including vector search latency, foundation model (FM) response times, and document ingestion rates. Set up anomaly detection for the metrics to proactively identify performance degradation before users are affected. 

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Question # 4

A retail company runs an application that makes product recommendations to customers on the company’s website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement?

A. Use an Amazon Bedrock Guardrails filter on the prompt. Set the input filter strength to HIGH. 
B. Generate multiple response variants that include the promotional message in different ways. Use a reranker model to select the most coherent version based on relevance to the original prompt. 
C. Run the prompt through Amazon Bedrock. Process the response through Amazon Bedrock AgentCore to add the promotional message. Rerank the results by using the original prompt and the desired message as context. 
D. Reinforce the requirement to include the new promotional message within product recommendations by using an output indicator in prompts to the FM. 

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Question # 5

A logistics company is building an agentic GenAI-powered solution to automate freight optimization. The solution must retrieve data in real time from multiple internal and external systems. The solution must include a human-in-the-loop approval step before the optimization process is finished. The solution must support modular growth as the number of integrations and amount of logic increases. Which solution will meet these requirements with the LEAST operational overhead?

A. Use an Amazon SageMaker AI endpoint that hosts a large language model (LLM) that directly calls all internal databases and external APIs. Use a custom web application that provides a UI to implement the human-in-the-loop review step. 
B. Build a hierarchical system by using the Strands Agents SDK and Amazon Bedrock AgentCore. Configure a coordinating agent to delegate tasks to multiple specialized agents. Use MCP to facilitate inter-agent messaging. Use AWS Step Functions to implement a human-in-the-loop approval step. 
C. Use AWS Glue to aggregate operational data into Amazon S3. Use Amazon Athena to query the data. Invoke an AWS Lambda function to generate route assignments. Use Amazon SNS to send notifications to supervisor agents. 
D. Use a single Amazon Bedrock AgentCore agent with AWS Lambda-based tools to integrate with all internal and external systems. Use AWS Step Functions to orchestrate the approval workflow. 

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Question # 6

A healthcare company uses a multi-agent system on Amazon Bedrock AgentCore. The system uses multiple FMs to process 2,000 medical report documents daily. The documents range from 5 to 50 pages each. The company discovers that the system is not performing complete analysis on documents that exceed 30 pages. Documents over 30 pages miss critical information from later sections. The results are truncated, but there are no explicit errors. The company must resolve this issue while maintaining response times under 10 seconds. The company must keep processing costs low. Which solution will meet these requirements?

A. Use Amazon CloudWatch Logs Insights queries to analyze Amazon Bedrock agent processing times. Increase the timeout settings for the Lambda functions that host the agents. Implement pagination for larger documents to process in sequential batches. 
B. Implement document chunking with semantic overlap between chunks where content from the end of one chunk appears at the beginning of the next chunk. Configure Amazon CloudWatch metrics to monitor InputTokenCount. Set up a CloudWatch dashboard for context utilization percentage. Configure diagnostic logging that identifies truncation points in the processing flow. 
C. Configure agent action groups to process documents sequentially by automatically creating separate tasks for each document section. Set up an AWS Step Functions workflow to coordinate processing. Combine the results from individual sections. 
D. Deploy the system in a higher memory configuration with a larger compute instance size to accommodate complete documents in memory. Add Amazon Comprehend Medical to identify and prioritize critical medical entities for improved context management. 

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Question # 7

A cooking spice company is launching a marketing campaign that will invite customers to upload videos of themselves cooking with the company's spices to a platform that the company operates. The company is designing a solution for the video-sharing platform. The solution must extract video transcripts and generate embeddings to help users search for videos based on spices, other ingredients, and cooking methods. The solution must integrate with the company's existing open-source chat system so users can interact with videos in real time. The solution must analyze user chat text for sentiment to determine the most positively received videos. Each month, the recipe videos that received the most positive feedback and the highest number of views will be displayed on the platform. Which combination of steps will meet these requirements with the LEAST operational overhead? (Select THREE.) 

A. Use Amazon Transcribe to perform automatic speech recognition (ASR) to extract video transcripts. Use Amazon Comprehend to perform sentiment analysis on chat feedback. Use an Amazon Bedrock embeddings model to generate embeddings. Store the embeddings in an Amazon Bedrock knowledge base to enable semantic retrieval. 
B. Use AWS Step Functions to orchestrate the entire application pipeline. Configure a step in the workflow to use an AWS Lambda function to call the chat system API. 
C. Configure a custom orchestration AWS Lambda function to call all application components directly, manage errors, and embed custom code in calls to the chat system API. 
D. Store video metrics such as positive feedback counts and view counts in Amazon DynamoDB. 
E. Use the Strands Agents SDK with built-in HTTP tools to call the open-source chat system API. Use the Strands Agents SDK built-in orchestration capabilities to coordinate calls to Amazon Bedrock Knowledge Bases to enable video search and to Amazon Comprehend to perform sentiment analysis. 
F. Store video metrics such as positive feedback counts and view counts in an Amazon Aurora PostgreSQL database that uses the pgvector extension. 

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Question # 8

A healthcare company runs a multi-step workflow that uses Amazon Bedrock foundation models (FMs) to assist clinicians with making patient clinical decisions. Recent data shows that 25% of decision processes fail to finish. However, the company cannot identify which specific workflow step is causing the failures. The company needs to implement a monitoring solution to identify the exact failure points in the workflow. The solution must provide detailed analytics for the completion rates of each workflow step. Which solution will meet these requirements?

A. Configure Amazon CloudWatch alarms to monitor the workflow's InvocationServerErrors API and InvocationClientErrors API error rates. Set up notifications for when errors exceed 25%. Configure custom metrics to track each workflow step separately. 
B. Enable AWS CloudTrail logging for the Amazon Bedrock FM workflow. Create scheduled Amazon Athena queries that analyze log patterns to identify which steps of the workflow fail most frequently. 
C. Enable tracing for InvokeAgent API requests to capture OrchestrationTrace and FailureTrace objects. Develop analytics on the structured trace data to calculate completion rates for each workflow step. 
D. Use Amazon EventBridge to capture workflow events for state transitions. Store the event data in Amazon S3. Use Amazon QuickSight to create dashboards that display completion rates for each workflow step. 

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Question # 9

A company uses Amazon Bedrock to deploy an application that generates technical documentation for users across multiple AWS Regions and in multiple languages. Users frequently submit semantically similar questions in different languages, which results in increased inference costs and response latency. The company needs a caching solution that significantly reduces inference costs, provides low-latency responses globally, maintains cache freshness with a 5-minute TTL, and minimizes custom cache key generation and application-managed caching logic. Which solution will meet these requirements?

A. Create a custom caching system that uses AWS Lambda functions to store inference results in an Amazon DynamoDB table. Use Amazon CloudFront to distribute cached responses to global users with a 5-minute TTL. 
B. Configure prompt caching in Amazon Bedrock for semantically similar queries across languages. Use Amazon CloudFront and Lambda@Edge functions to handle Regional cache distribution. Set a TTL of 5 minutes for both caching layers. 
C. Create Amazon ElastiCache (Redis OSS) clusters in each Region where the application runs to store inference results with custom fingerprinting for multilingual queries. Configure automatic replication between Regional clusters with a 5-minute TTL. 
D. Use Amazon DynamoDB Accelerator (DAX) to cache inference results and to automatically manage TTL. Use Amazon CloudFront to distribute API responses globally. Use edge functions to handle language-specific transformations. 

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Question # 10

A financial services company uses Amazon Bedrock to analyze customer data that is stored in an Amazon S3 bucket. The data includes personally identifiable information (PII). The company must mask PII from foundation model (FM) responses. Which solution will meet this requirement with the LEAST operational effort?

A. Create a guardrail in Amazon Bedrock to filter PII content. Define the PII type and set the guardrail action to MASK. Configure Amazon Bedrock to apply the filter to each FM response. 
B. Use Amazon Comprehend to detect PII entities in the S3 data before invoking Amazon Bedrock. Configure an AWS Lambda function to call the Amazon Comprehend DetectPiiEntities API to mask detected PII. Store the processed data back to the original S3 bucket. 
C. Use Amazon Macie to scan the S3 bucket for PII data. Configure an AWS Lambda function to store PII in a second S3 bucket. Use an Amazon EventBridge rule to invoke the Lambda function. 
D. Configure an AWS Lambda function to search for PII data. Implement a step in the Lambda function code to store the PII in a second S3 bucket and non-PII data into a third S3 bucket. 

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Question # 11

A healthcare company creates a custom foundation model (FM) that uses a proprietary architecture to summarize and answer questions about sensitive patient records and conversations. To comply with regulations, the company must ensure confidentiality by implementing extensive monitoring and controls. The company must verify the accuracy of the FM by checking prompts and responses for hallucinations. Which solution will meet these requirements?

A. Use the Custom Model Import feature in Amazon Bedrock to import the FM. Configure Amazon Bedrock guardrails that apply content filters with high thresholds for grounding and relevance. 
B. Use Amazon SageMaker Serverless Inference to host the model. Configure Amazon Bedrock guardrails that apply contextual grounding checks with high thresholds for grounding and relevance. Use custom application code that routes prompts and responses through the guardrails. 
C. Use Amazon SageMaker JumpStart to import the FM to Amazon Bedrock. Configure Amazon Bedrock guardrails that apply content filters with high thresholds for grounding and relevance. 
D. Use the Custom Model Import feature in Amazon Bedrock to import the FM. Configure AWS HealthScribe to apply contextual grounding check rules to comply with regulatory requirements. 

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Question # 12

A financial services company provides an Amazon Bedrock-powered AI assistant that provides investment guidance to customers. Recent audits found racially and genderbiased responses. Auditors flagged several cases as potential regulatory compliance risks. The company must implement a 30-day remediation plan to eliminate biased outputs. The plan must preserve model accuracy and keep latency within acceptable customerexperience thresholds. A GenAI developer must design a long-term solution that balances regulatory risks, operational scalability, and model-agnostic enforcement. The solution must apply enforceable at-inference-time controls so bias mitigation measures cannot be bypassed. The solution must provide logging for each inference for audit and regulatory reviews. The solution must support ongoing automated bias monitoring. The solution must not require any model re-training within 30 days. The solution must add minimal inference latency. Which solution will meet these requirements?

A. Use Amazon SageMaker Clarify to generate offline bias reports. Schedule weekly manual reviews with compliance teams to evaluate fairness trends and recommend model adjustments. 
B. Use AWS Lambda to build a custom fairness classifier that evaluates model outputs. Store classifier scores in Amazon DynamoDB. Block responses that fall below the fairness threshold. 
C. Use AWS Lambda to build a custom fairness classifier that evaluates model outputs against demographic fairness criteria. Store classifier scores in Amazon DynamoDB. Track metadata for the scores. Block responses that fall below the fairness threshold. Route flagged cases to compliance teams for manual reviews before delivering responses to customers. 
D. Route all responses through Amazon API Gateway and AWS WAF. Configure AWS WAF to use a custom managed rule group to detect biased terms and to block disallowed responses. Use AWS CloudTrail Lake to analyze long-term traffic trends. 

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Question # 13

A financial services company is using Amazon Bedrock to deploy a GenAI application across multiple business units. The company must ensure that all prompts that are used with the application's FMs follow regulatory compliance standards and maintain consistent formatting. The company must implement a solution that provides version control for prompt templates, requires approval workflows for new prompts, and maintains detailed audit trails of all prompt usage and modifications. Which combination of solutions will meet these requirements? (Select TWO.)

A. Use Amazon Bedrock Prompt Management to create parameterized prompt templates and enable version control. Configure approval workflows that require approvals from the company's regulatory compliance team before deploying new prompts to production environments. 
B. Store all prompt templates in Amazon S3 buckets. Enable versioning on the buckets. Use AWS Lambda functions and Amazon SNS to send approval requests to regulatory compliance reviewers through email notifications. 
C. Configure AWS CloudTrail to log all Amazon Bedrock API calls and prompt interactions. Use Amazon CloudWatch Logs to capture detailed access patterns and prompt usage metrics to generate compliance reports. 
D. Use AWS Systems Manager Parameter Store to manage prompt templates as secure strings. Use AWS Step Functions to orchestrate a multi-stage approval workflow that has automatic rollback capabilities. 
E. Use Amazon API Gateway and AWS WAF rules to filter prompt requests. Use Amazon DynamoDB to store approval status and audit information for all prompt template modifications. 

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Question # 14

A GenAI developer is using Amazon Bedrock AgentCore to build an agentic AI application. The application orchestrates multiple foundation models (FMs) across development and production environments. The application experiences intermittent failures during tool invocation phases. Each incident requires an average of 3 hours to diagnose because of complex error patterns in the FM chains. The GenAI developer needs a solution that can identify AI-specific error patterns in tool invocation chains in less time than the existing manual process. The solution must support collaboration between development and security teams. The solution must provide capabilities to protect sensitive customer data. Which solution will meet these requirements? 

A. Create an Amazon CloudWatch dashboard that displays detailed Amazon Bedrock API metrics. Integrate the dashboard with Amazon GuardDuty. Use CloudWatch Logs Insights to perform error analysis. 
B. Use Kiro as the integrated development environment (IDE) to directly access both application codebases and trace logs. Enable real-time analysis of error patterns directly in the IDE by analyzing both application code and trace logs together to understand how the agents orchestrate tool invocations. 
C. Set up AWS X-Ray tracing for all AWS Lambda functions that handle model invocations. Use Amazon OpenSearch Service to create a custom monitoring solution with OpenSearch dashboard visualizations. 
D. Enable Amazon Bedrock AgentCore Observability with AWS Distro for OpenTelemetry instrumentation. Use Amazon CloudWatch Generative AI Observability to perform trace analysis and to monitor tool invocations. 

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Question # 15

A global research company is building a RAG-enabled AI system that uses Amazon Bedrock Knowledge Bases. The company stores documents in Amazon S3 and indexes the documents into an Amazon OpenSearch Serverless vector collection. When the company evaluates the system, the company identifies three issues. Queries return outdated documents when users request only recent research. Medical research queries return both medical research documents and engineering domain documents. Users can retrieve documents that were authored by researchers who the users should not have access to based on company policies. The company wants to improve the system so that retrieval becomes more precise and contextually appropriate. Which solution will meet these requirements?

A. Add custom metadata fields to the documents in Amazon S3 to record timestamp, authorship, and research domain. Index the document embeddings and the custom metadata fields into the OpenSearch Serverless vector collection. At query time, use the knowledge base to run vector similarity search and return the stored metadata with the results to help the model interpret document relevance. 
B. Use S3 object metadata to store each document's timestamp. Use a custom metadata field to record authorship. Use S3 object tags to record the research domain. Propagate the metadata fields into the OpenSearch Serverless vector collection as filterable attributes. Use Knowledge Bases to apply timestamp, author, and domain filters before running vector similarity search. 
C. Store documents in Amazon S3. Extract timestamp, author metadata, and researchdomain tags, and store the data in an Amazon DynamoDB table. During retrieval, apply author, domain, and timestamp filters in DynamoDB to identify candidate document IDs. Use the filtered document IDs to narrow the vector similarity search in the OpenSearch Serverless collection. 
D. Store documents in Amazon S3 with custom metadata to record authorship. Use Amazon Comprehend to classify each document into a research domain. Store the classification results in Amazon Aurora. Query Aurora during retrieval to identify relevant domains before performing vector similarity search in the OpenSearch Serverless collection. 

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Amazon AIP-C01 Reviews