AIP-C01 practice questions 51–60

1. Question 51 · 100 in bank

   A company uses Amazon Bedrock to develop an AI assistant to provide customer support. Analysis shows that 40% of customer queries use varied phrasing or wording to ask the same questions. The company wants a solution to reduce redundant model calls. The solution must ensure that semantically equivalent questions receive consistent answers. The solution must ensure low latency. Which solution will meet these requirements?

   • ADeploy an Amazon DynamoDB Accelerator (DAX) cluster as an in-memory cache. Specify the query text as the partition key and the model response text as the sort key. Query the cache by using a filter expression with the LIKE operator.
   • BUse Amazon Bedrock to generate embeddings from customer queries. Use Amazon MemoryDB for Valkey to store hash sets of vector embeddings and model responses. Use a RANGE query to find similar queries and their responses.
   • CDeploy Amazon OpenSearch Service that has k-nearest neighbor (k-NN) capabilities to store query-response text pairs. Use an approximate k-NN technique to find similar queries and their responses.
   • DCreate a caching solution by using Amazon DynamoDB to create a global secondary index on the normalized query text. Apply stemming to incoming queries. Query the index of cached customer queries.

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   Answer: C

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2. Question 52 · 100 in bank

   A company uses AWS Lambda functions to build an AI agent solution. A GenAI developer must set up a Model Context Protocol (MCP) server that accesses user information. The GenAI developer must also configure the AI agent to use the new MCP server. The GenAI developer must ensure that only authorized users can access the MCP server. Which solution will meet these requirements?

   • AUse a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent's MCP client to invoke the MCP server asynchronously.
   • BUse a Lambda function to host the MCP server. Grant the AI agent Lambda functions permission to invoke the Lambda function that hosts the MCP server. Configure the AI agent to use the STDIO transport with the MCP server.
   • CUse a Lambda function to host the MCP server. Create an Amazon API Gateway HTTP API that proxies requests to the Lambda function. Configure the AI agent solution to use the Streamable HTTP transport to make requests through the HTTP API. Use Amazon Cognito to enforce OAuth 2.1.
   • DUse a Lambda layer to host the MCP server. Add the Lambda layer to the AI agent Lambda functions. Configure the agentic AI solution to use the STDIO transport to send requests to the MCP server. In the AI agent's MCP configuration, specify the Lambda layer ARN as the command. Specify the user credentials as environment variables.

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   Answer: C

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3. Question 53 · 100 in bank

   A company wants to create an annual rewards program for its customers. The rewards that customers earn vary based on different parameters such as the categories of the items ordered and the customers' purchase history. The company needs a generative AI (GenAI) solution that uses three Amazon Bedrock agents to help customers during online catalog browsing. The agents must use knowledge bases and action groups to handle the search, recommendation, and order modules. The modules must operate sequentially. An AWS Lambda function must calculate estimated rewards for each recommended item. The solution must provide graceful degradation during service disruptions. Which solution will meet these requirements with the MOST operational efficiency?

   • ADefine an Amazon API Gateway REST API behind each agent. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Configure the second Lambda function with a retry/fallback mechanism.
   • BCreate an AWS Step Functions state machine with four tasks that run the agents and the rewards Lambda function. Set up retry and catch branches for each of the task steps.
   • CConfigure each agent with a separate retry/fallback mechanism. Create a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Define an Amazon API Gateway REST API behind the second Lambda function.
   • DCreate a second Lambda function to orchestrate the calls to the agents and the rewards Lambda function. Create an AWS Step Functions state machine with one task that runs the second Lambda function. Set up retry and catch branches for the task step.

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   Answer: B

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4. Question 54 · 100 in bank

   A company is creating a workflow to review customer-facing communications before the company sends the communications. The company uses a pre-defined message template to generate the communications and stores the communications in an Amazon S3 bucket. The workflow needs to capture a specific portion from the template and send it to an Amazon Bedrock model. The workflow must store model responses back to the original S3 bucket. Which solution will meet these requirements?

   • ACreate a flow in Amazon Bedrock Flows. Configure S3 action nodes at the beginning and end of the flow to retrieve and store the communications and the model responses. In the middle of the flow, configure an expression to parse each communication. Configure an agent step to send the parsed input to the model for review.
   • BCreate an AWS Step Functions Express workflow state machine. Use an Amazon S3 integration GetObject step to retrieve the original communications. Use an intrinsic function Pass step to parse the communications and to pass the results to an Amazon Bedrock InvokeModel step. Configure an Amazon S3 integration PutObject step to store the model responses back to the S3 bucket.
   • CCreate an Amazon Bedrock agent that has an action group. Configure instructions to define how the agent should parse the communications. Configure the action group to retrieve the communications from the S3 bucket, invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.
   • DCreate an Amazon Bedrock agent that has a single action group. Configure three AWS Lambda functions in the action group. Configure the functions to retrieve the communications from the S3 bucket, parse the communications and invoke the Amazon Bedrock model, and store the model responses back to the S3 bucket.

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   Answer: B

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5. Question 55 · 100 in bank

   A financial technology company is using Amazon Bedrock to build an assessment system for the company's customer service AI assistant. The AI assistant must provide financial recommendations that are factually accurate, compliant with financial regulations, and conversationally appropriate. The company needs to combine automated quality evaluations at scale with targeted human reviews of critical interactions. What solution will meet these requirements?

   • AConfigure a pipeline in which financial experts manually score all responses for accuracy, compliance, and conversational quality. Use Amazon SageMaker notebooks to analyze results to identify improvement areas.
   • BConfigure Amazon Bedrock evaluations that use Anthropic Claude Sonnet as a judge model to assess response accuracy and appropriateness. Configure custom Amazon Bedrock guardrails to check responses for compliance with financial policies. Add Amazon Augmented AI (Amazon A2I) human reviews for flagged critical interactions.
   • CCreate an Amazon Lex bot to manage the customer service interactions. Configure AWS Lambda functions to check responses against a static compliance database. Configure intents in the bot that call the Lambda functions to check the responses. Add an additional intent to collect end-user reviews.
   • DConfigure Amazon CloudWatch to monitor response patterns from the AI assistant. Configure CloudWatch alerts for potential compliance violations. Establish a team of human evaluators to review flagged interactions.

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   Answer: B

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6. Question 56 · 100 in bank

   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?

   • AIsolate 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.
   • BCreate 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.
   • CIsolate 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.
   • DImplement 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.

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   Answer: A

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7. Question 57 · 100 in bank

   A company uses an AI assistant application to summarize the company's website content and provide information to customers. The company plans to use Amazon Bedrock to give the application access to a foundation model (FM). The company needs to deploy the AI assistant application to a development environment and a production environment. The solution must integrate the environments with the FM. The company wants to test the effectiveness of various FMs in each environment. The solution must provide product owners with the ability to easily switch between FMs for testing purposes in each environment. Which solution will meet these requirements?

   • ACreate one AWS CDK application. Create multiple pipelines in AWS CodePipeline. Configure each pipeline to have its own settings for each FM. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.ProvisionedModel.fromProvisionedModelArn() method.
   • BCreate a separate AWS CDK application for each environment. Configure the applications to invoke the Amazon Bedrock FMs by using the aws_bedrock.FoundationModel.fromFoundationModelId() method. Create a separate pipeline in AWS CodePipeline for each environment.
   • CCreate one AWS CDK application. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.FoundationModel.fromFoundationModelId() method. Create a pipeline in AWS CodePipeline pipeline that has a deployment stage for each environment that uses AWS CodeBuild deploy actions.
   • DCreate one AWS CDK application for the production environment. Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.ProvisionedModel.fromProvisionedModelArn() method. Create a pipeline in AWS CodePipeline. Configure the pipeline to deploy to the production environment by using an AWS CodeBuild deploy action. For the development environment, manually recreate the resources by referring to the production application code.

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   Answer: C

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8. Question 58 · 100 in bank

   A hotel company wants to enhance a legacy Java-based property management system (PMS) by adding AI capabilities. The company wants to use Amazon Bedrock Knowledge Bases to provide staff with room availability information and hotel-specific details. The solution must maintain separate access controls for each hotel that the company manages. The solution must provide room availability information in near real time and must maintain consistent performance during peak usage periods. Which solution will meet these requirements?

   • ADeploy a single Amazon Bedrock knowledge base that contains combined data for all hotels. Configure AWS Lambda functions to synchronize data from each hotel's PMS database through direct API connections. Implement AWS CloudTrail logging with hotel-specific filters to audit access logs for each hotel's data.
   • BCreate an Amazon EventBridge rule for each hotel that is invoked by changes to the PMS database for each hotel. Configure the rule to send updates to a centralized Amazon Bedrock knowledge base in a management AWS account. Configure resource-based policies to enforce hotel-specific access controls for hotel staff.
   • CImplement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide real-time room availability information. Schedule regular synchronization for less critical information.
   • DBuild a centralized Amazon Bedrock agent that uses multiple knowledge bases. Implement AWS IAM Identity Center with hotel-specific permission sets to control hotel staff data access.

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   Answer: C

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9. Question 59 · 100 in bank

   A company is implementing a serverless inference API by using AWS Lambda. The API will dynamically invoke multiple AI models hosted on Amazon Bedrock. The company needs to design a solution that can switch between model providers without modifying or redeploying Lambda code in real time. The design must include safe rollout of configuration changes and validation and rollback capabilities. Which solution will meet these requirements?

   • AStore the active model provider in AWS Systems Manager Parameter Store. Configure a Lambda function to read the parameter at runtime to determine which model to invoke.
   • BStore the active model provider in AWS AppConfig. Configure a Lambda function to read the configuration at runtime to determine which model to invoke.
   • CConfigure an Amazon API Gateway REST API to route requests to separate Lambda functions. Hardcode each Lambda function to a specific model provider. Switch the integration target manually.
   • DStore the active model provider in a JSON file hosted on Amazon S3. Use AWS AppConfig to reference the S3 file as a hosted configuration source. Configure a Lambda function to read the file through AppConfig at runtime to determine which model to invoke.

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   Answer: B

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10. Question 60 · 100 in bank

    A company is building a generative AI (GenAI) application that uses Amazon Bedrock APIs to process complex customer inquiries. During peak usage periods, the application experiences intermittent API timeouts that cause issues such as broken response chunks and delayed data delivery. The application struggles to ensure that prompts remain within token limits when handling complex customer inquiries of varying lengths. Users have reported truncated inputs and incomplete responses. The company has also observed foundation model (FM) invocation failures. The company needs a retry strategy that automatically handles transient service errors and prevents overwhelming Amazon Bedrock during peak usage periods. The strategy must also adapt to changing service availability and support response streaming and token-aware request handling. Which solution will meet these requirements?

    • AImplement a standard retry strategy that uses a 1-second fixed delay between attempts and a 3-retry maximum for all errors. Handle streaming response timeouts by restarting streams. Cap token usage for each session.
    • BImplement an adaptive retry strategy that uses exponential backoff with jitter and a circuit breaker pattern that temporarily disables retries when error rates exceed a predefined threshold. Implement a streaming response handler that monitors for chunk delivery timeouts. Configure the handler to buffer successfully received chunks and intelligently resume streaming from the last received chunk when connections are re-established.
    • CUse the AWS SDK to configure a retry strategy in standard mode. Wrap Amazon Bedrock API calls in try-catch blocks that handle timeout exceptions. Return cached completions for failed streaming requests. Enforce a global token limit for all users. Add jitter-based retry logic and lightweight token trimming for each request. Resume broken streams by requesting only the missing chunks from the point of failure. Maintain a small in-memory buffer of the most recent chunks to minimize redundant data transfer.
    • DSet Amazon Bedrock client request timeouts to 30 seconds. Implement client-side load shedding. Buffer partial results and stop new requests when the application performance begins to degrade. Set static token usage caps for all requests. Configure exponential backoff retries, dynamic chunk sizing, and context-aware token limits.

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    Answer: B

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AIP-C01 practice, question 51. 100 questions available.