Amazon AIP-C01 Exam Dumps

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Amazon AIP-C01 Sample Questions

Question # 1

A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models. The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic. The company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change. Which solution will meet these requirements? 

A. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch dashboards for token metrics. Configure static CloudWatch alarms with fixed thresholds for each tool integration.
 B. Store model invocation logs in Amazon S3. Use AWS Glue and Amazon Athena to analyze token usage trends. 
C. Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch metric filters to extract tool-specific invocation patterns. Apply CloudWatch anomaly detection alarms that automatically adjust baselines for each tool’s token metrics. 
D. Store model invocation logs in an Amazon S3 bucket. Use AWS Lambda to process logs in real time. Manually update CloudWatch alarm thresholds based on trends identified by the Lambda function. 

Answer: C

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Question # 2

An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations. The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company’s product catalog. The company must resolve these issues. Which solution will meet this requirement? 

A. Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput. 
B. Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers. 
C. Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation RAG. Set the PerformanceConfigLatency parameter to optimized. 
D. Store product catalog data in Amazon OpenSearch Service. Validate the model’s product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching. 

Answer: C

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Question # 3

A company runs a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock Knowledge Bases to perform regulatory compliance queries. The application uses the RetrieveAndGenerateStream API. The application retrieves relevant documents from a knowledge base that contains more than 50,000 regulatory documents, legal precedents, and policy updates. The RAG application is producing suboptimal responses because the initial retrieval often returns semantically similar but contextually irrelevant documents. The poor responses are causing model hallucinations and incorrect regulatory guidance. The company needs to improve the performance of the RAG application so it returns more relevant documents. Which solution will meet this requirement with the LEAST operational overhead?

A. Deploy an Amazon SageMaker endpoint to run a fine-tuned ranking model. Use an Amazon API Gateway REST API to route requests. Configure the application to make requests through the REST API to rerank the results. 
B. Use Amazon Comprehend to classify documents and apply relevance scores. Integrate the RAG application’s reranking process with Amazon Textract to run document analysis. Use Amazon Neptune to perform graph-based relevance calculations.
C. Implement a retrieval pipeline that uses the Amazon Bedrock Knowledge Bases Retrieve API to perform initial document retrieval. Call the Amazon Bedrock Rerank API to rerank the results. Invoke the InvokeModelWithResponseStream operation to generate responses. 
D. Use the latest Amazon reranker model through the reranking configuration within Amazon Bedrock Knowledge Bases. Use the model to improve document relevance scoring and to reorder results based on contextual assessments. 

Answer: D

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Question # 4

A company is building a serverless application that uses AWS Lambda functions to help students around the world summarize notes. The application uses Anthropic Claude through Amazon Bedrock. The company observes that most of the traffic occurs during evenings in each time zone. Users report experiencing throttling errors during peak usage times in their time zones. The company needs to resolve the throttling issues by ensuring continuous operation of the application. The solution must maintain application performance quality and must not require a fixed hourly cost during low traffic periods. Which solution will meet these requirements? 

A. Create custom Amazon CloudWatch metrics to monitor model errors. Set provisioned throughput to a value that is safely higher than the peak traffic observed. 
B. Create custom Amazon CloudWatch metrics to monitor model errors. Set up a failover mechanism to redirect invocations to a backup AWS Region when the errors exceed a specified threshold. 
C. Enable invocation logging in Amazon Bedrock. Monitor key metrics such as Invocations, InputTokenCount, OutputTokenCount, and InvocationThrottles. Distribute traffic across cross-Region inference endpoints. 
D. Enable invocation logging in Amazon Bedrock. Monitor InvocationLatency, InvocationClientErrors, and InvocationServerErrors metrics. Distribute traffic across multiple versions of the same model. 

Answer: C

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Question # 5

A healthcare company is using Amazon Bedrock to develop a real-time patient care AI assistant to respond to queries for separate departments that handle clinical inquiries, insurance verification, appointment scheduling, and insurance claims. The company wants to use a multi-agent architecture. The company must ensure that the AI assistant is scalable and can onboard new features for patients. The AI assistant must be able to handle thousands of parallel patient interactions. The company must ensure that patients receive appropriate domain-specific responses to queries. Which solution will meet these requirements?

A. Isolate data for each agent by using separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a supervisor agent to perform natural language intent classification on patient inquiries. Configure the supervisor agent to route queries to specialized collaborator agents to respond to department-specific queries. Configure each specialized collaborator agent to use Retrieval Augmented Generation (RAG) with the agent’s department-specific knowledge base. 
B. Create a separate supervisor agent for each department. Configure individual collaborator agents to perform natural language intent classification for each specialty domain within each department. Integrate each collaborator agent with department-specific knowledge bases only. Implement manual handoff processes between the supervisor agents. 
C. Isolate data for each department in separate knowledge bases. Use IAM filtering to control access to each knowledge base. Deploy a single general-purpose agent. Configure multiple action groups within the general-purpose agent to perform specific department functions. Implement rule-based routing logic within the general-purpose agent instructions. 
D. Implement multiple independent supervisor agents that run in parallel to respond to patient inquiries for each department. Configure multiple collaborator agents for each supervisor agent. Integrate all agents with the same knowledge base. Use external routing logic to merge responses from multiple supervisor agents. 

Answer: A

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Question # 6

A company has a generative AI (GenAI) application that uses Amazon Bedrock to provide real-time responses to customer queries. The company has noticed intermittent failures with API calls to foundation models (FMs) during peak traffic periods. The company needs a solution to handle transient errors and provide detailed observability into FM performance. The solution must prevent cascading failures during throttling events and provide distributed tracing across service boundaries to identify latency contributors. The solution must also enable correlation of performance issues with specific FM characteristics. Which solution will meet these requirements?

A. Implement a custom retry mechanism with a fixed delay of 1 second between retries. Configure Amazon CloudWatch alarms to monitor the application’s error rates and latency metrics. 
B. Configure the AWS SDK with standard retry mode and exponential backoff with jitter. Use AWS X-Ray tracing with annotations to identify and filter service components. 
C. Implement client-side caching of all FM responses. Add custom logging statements in the application code to record API call durations. 
D. Configure the AWS SDK with adaptive retry mode. Use AWS CloudTrail distributed tracing to monitor throttling events. 

Answer: B

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