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AWS Generative AI Developer Professional Dumps (AIP-C01) - AWS Exam Questions
Last updated on July 22, 2026
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1
Question
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?
Which solution will meet these requirements?
Options
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.
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2
Question
An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application. The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce detailed product descriptions. The workflow consists of three sequential states: a description generator, a technical specifications validator, and a brand voice consistency checker. Each state produces intermediate reasoning traces and outputs that are passed to the next state. The application uses an Amazon S3 bucket for process storage and to store outputs. During testing, the company discovers that outputs between Step Functions states frequently exceed the 256 KB quota and cause workflow failures. A GenAI Developer needs to revise the application architecture to efficiently handle the Step Functions 256 KB quota and maintain workflow observability. The revised architecture must preserve the existing multi-agent reasoning and acting (ReAct) pattern.
Which solution will meet these requirements with the LEAST operational overhead?
Which solution will meet these requirements with the LEAST operational overhead?
Options
A
Store intermediate outputs in Amazon DynamoDB. Pass only references between states. Create a Map state that retrieves the complete data from DynamoDB when required for each agent's processing step.
B
Use AWS Lambda functions to compress outputs to less than 256 KB before each agent state.
Configure each agent task to decompress outputs before processing and to compress results before passing them to the next state.
Configure each agent task to decompress outputs before processing and to compress results before passing them to the next state.
C
Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameters for large outputs. Use the ResultPath and ResultSelector fields to route S3 references between the agent steps while maintaining the sequential validation workflow.
D
Configure a separate Step Functions state machine to handle each agent’s processing. Use Amazon EventBridge to coordinate the execution flow between state machines. Use S3 references
for the outputs as event data.
for the outputs as event data.
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3
Question
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
Which solution will meet these requirements?
Options
A
Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.
B
Configure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent
for each user request. Run business logic in the Lambda function to select the appropriate FM for
each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
for each user request. Run business logic in the Lambda function to select the appropriate FM for
each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
C
Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables.
Update the variables when the system switches between models.
Update the variables when the system switches between models.
D
Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.
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4
Question
A healthcare company is developing an application to process medical queries. The application must answer complex queries with high accuracy by reducing semantic dilution. The application must refer to domain-specific terminology in medical documents to reduce ambiguity in medical terminology.
The application must be able to respond to 1,000 queries each minute with response times less than
2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
The application must be able to respond to 1,000 queries each minute with response times less than
2 seconds.
Which solution will meet these requirements with the LEAST operational overhead?
Options
A
Use Amazon API Gateway to route incoming queries to an Amazon Bedrock agent. Configure the agent to use an Anthropic Claude model to decompose queries and an Amazon Titan model to expand queries. Create an Amazon Bedrock knowledge base to store the reference medical documents.
B
Configure an Amazon Bedrock knowledge base to store the reference medical documents. Enable query decomposition in the knowledge base. Configure an Amazon Bedrock flow that uses a foundation model and the knowledge base to support the application.
C
Use Amazon SageMaker AI to host custom ML models for both query decomposition and query expansion. Configure Amazon Bedrock knowledge bases to store the reference medical documents.
Encrypt the documents in the knowledge base.
Encrypt the documents in the knowledge base.
D
Create an Amazon Bedrock agent to orchestrate multiple AWS Lambda functions to decompose queries. Create an Amazon Bedrock knowledge base to store the reference medical documents. Use the agent’s built-in knowledge base capabilities. Add deep research and reasoning capabilities to the agent to reduce ambiguity in the medical terminology.
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5
Question
A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between multiple Amazon Bedrock models based on real-time inference metrics, historical traffic patterns, and service health. The solution must be able to gradually increase traffic to new model versions. The system must increase traffic if metrics remain healthy and decrease traffic if the performance degrades below acceptable thresholds. The company needs to comprehensively monitor inference latency and error rates during the deployment phase. The company must also be able to halt deployments and revert to a previous model version without any manual intervention.
Which solution will meet these requirements?
Which solution will meet these requirements?
Options
A
Use Amazon Bedrock with provisioned throughput to host model versions. Configure an Amazon EventBridge rule to invoke an AWS Step Functions workflow when a new model version is released.
Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds.
Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds.
B
Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions. Use variant weights to shift traffic. Use Amazon CloudWatch and SageMaker Model Monitor to trigger rollbacks. Use EventBridge to roll back deployments if an anomaly is detected.
C
Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon API Gateway HTTP API with stage variables and weighted routing to shift traffic gradually. Use Amazon CloudWatch to monitor performance. Use external logic to adjust traffic and roll back if performance falls below thresholds.
D
Use Amazon OpenSearch Service to track inference logs. Configure OpenSearch Service to invoke an AWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shift traffic based on inference logs.
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