AIP-C01 Exam: Absolutely Free Online Practice

Question 1
- A manufacturing company uses a legacy on-premises ERP system to track assembly line issues. The company wants to integrate Amazon Bedrock to automatically analyze "Technician Notes" (unstructured text) and categorize the root cause of failures using the Anthropic Claude 4.5 Sonnet model.The legacy ERP system has a strict limitation: it blocks all user operations while waiting for external API responses and times out if a response takes longer than 300 milliseconds. The GenAI analysis typically takes 3–5 seconds to complete.As a GenAI Developer, which integration architecture should you design to incorporate the Foundation Model (FM) capabilities without disrupting the legacy system's operations?
  A : Use AWS AppSync with a GraphQL subscription. configure the ERP system to subscribe to the RootCauseAnalysis mutation. When a note is saved, trigger the mutation to invoke Amazon Bedrock directly. The subscription connection keeps the channel open without blocking the main ERP thread, allowing the response to stream back when ready.
  
  B : Create an Amazon API Gateway REST API with a synchronous integration to an AWS Lambda function. Configure the ERP to call this API with the notes. Update the Lambda function to increase the memory allocation to 10 GB, ensuring the model inference completes within the 300-millisecond window.
  
  C : Implement an asynchronous, event-driven architecture. Configure the ERP to push the technician notes to an Amazon SQS queue and immediately return success to the user. Use an AWS Lambda function to poll the queue, invoke the Amazon Bedrock model for analysis, and write the classification results back to the ERP database via a separate callback API.
  
  D : Deploy an AWS PrivateLink connection between the on-premises network and the Amazon Bedrock service endpoint. Configure the ERP system to make direct HTTPS calls to the Bedrock InvokeModel API using the private endpoint, bypassing the public internet to reduce network latency below the 300-millisecond threshold.
  
  Answer: C
Question 2
- 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 3
- A travel company is building a complex "Vacation Planner" system using Amazon Bedrock Agents multi-agent collaboration:Flight Specialist Agent: Searches flight databases using a Lambda-based action groupHotel Specialist Agent: Searches hotel inventory using an API-based action groupSupervisor Agent: Interacts with the user, breaks down the "Plan a trip to Paris" request, delegates sub-tasks to the specialist agents, and aggregates their findings into a final itineraryThe developer needs to optimize the system to reduce latency for simple, single-domain queries (like "Find me a hotel in Paris") while maintaining the ability to handle complex multi-step requests (like "Plan a complete 5-day vacation to Paris").Which collaboration mode configuration meets this requirement?
  A : Configure the supervisor agent in Supervisor with routing mode. Simple queries are routed directly to the appropriate collaborator agent (Flight or Hotel Specialist), while complex queries automatically trigger full supervisor mode for orchestration across multiple agents.
  
  B : Configure the supervisor agent in Supervisor mode. The supervisor always breaks down the request, invokes collaborator agents in parallel, and synthesizes the results regardless of query complexity.
  
  C : Deploy all three agents as independent agents without multi-agent collaboration. Use Amazon Lex to parse user intent and route to the appropriate single agent, then use a Lambda function to aggregate results from multiple agent invocations.
  
  D : Use AWS Step Functions to create a Choice state that evaluates query complexity. Route simple queries to individual agents via direct Lambda invocation, and complex queries to the Bedrock supervisor agent.
  
  Answer: A
Question 4
- A financial analyst uses an internal GenAI tool to extract data from quarterly earnings PDFs into a standardized JSON format. The application is reporting frequent ValidationError exceptions in the backend. Upon investigation, the developer notices that for certain complex PDFs, the model either omits required fields or outputs the data as a Markdown table instead of JSON, despite the system prompt instructions.Which troubleshooting and remediation workflow should the developer implement to resolve these prompt maintenance issues and prevent recurrence?
  A : Enable model invocation logging to Amazon CloudWatch Logs to capture the raw inputs and outputs. Use CloudWatch Logs Insights to filter for the failed requests and identify the specific document patterns causing the prompt confusion. Create a test suite of these edge cases, iterate on the prompt template by adding "few-shot" examples of the correct JSON structure, and implement strict schema validation to detect format inconsistencies before processing.
  
  B : Configure AWS X-Ray to trace the latency of the model invocations. Identify the requests that take longer than 5 seconds to generate a response, as these indicate prompt confusion. Switch the Foundation Model to a lower-latency version (e.g., from Claude to Titan) to ensure the model responds before the application times out.
  
  C : Use Amazon SageMaker Model Monitor to track the statistical bias of the generated JSON output. Configure a baseline for the expected distribution of keys in the JSON object. Set up an alert to trigger if the drift in key frequency exceeds 10%, indicating a format inconsistency.
  
  D : Implement an Amazon EventBridge rule to trigger an automatic retry with a higher temperature setting (e.g., 1.0) whenever the backend reports a ValidationError. This encourages the model to generate a different, more creative output format that might pass validation on the second attempt.
  
  Answer: A
Question 5
- A financial services company wants to develop an Amazon Bedrock application that gives analysts the ability to query quarterly earnings reports and financial statements. The financial documents are typically 5–100 pages long and contain both tabular data and text. The application must provide contextually accurate responses that preserve the relationship between financial metrics and their explanatory text. To support accurate and scalable retrieval, the application must incorporate document segmentation and context management strategies.Which solution will meet these requirements?
  A : Use a direct model invocation approach that uses Anthropic Claude to process each financial documentas a single input. Use fine-tuned prompts that instruct the model to parse tables and text separately.
  
  B : Use Amazon Bedrock Knowledge Bases to create a Retrieval Augmented Generation (RAG)application that retrieves relevant information from contextually chunked sections of financialdocuments. Segment documents based on their structural layout. Include citations that reference theoriginal source materials.
  
  C : Deploy an Amazon Bedrock agent that has an action group that calls custom AWS Lambda functions toanalyze financial documents. Configure the Lambda functions to perform fixed-size chunking when auser submits a query about financial metrics.
  
  D : Create one specialized Amazon Bedrock application that is optimized for structured data. Create asecond application that is optimized for unstructured data. Configure each application to use a tailoredchunking strategy that is suited to the application's content type. Implement logic to link queries to theappropriate sources.
  
  Answer: B

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