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Free AWS Certified Generative AI Developer - Professional AIP-C01 Exam Braindumps
AWS Certified Generative AI Developer - Professional AIP-C01 Questions 17–24 (page: 4)

Updated On: 15-Apr-2026
Page 4 of 14
PDF with 97 Questions

A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. The analysts must be able to examine the relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities. The application must produce responses in less than 3 seconds.

Which solution will meet these requirements with the LEAST operational overhead?

  1. Use Amazon Bedrock Knowledge Bases with Graph RAG and Amazon Neptune Analytics to store the financial data. Analyze the multi-hop relationships between entities and automatically identify related information across documents.
  2. Use Amazon Bedrock Knowledge Bases and an Amazon OpenSearch Service vector store to implement custom relationship identification logic that uses AWS Lambda functions to query multiple vector embeddings in sequence.
  3. Use an Amazon OpenSearch Serverless vector database with k-nearest neighbor (k-NN) searches.
    Implement manual relationship mapping in an application layer that runs in an Amazon EC2 Auto Scaling group.
  4. Use Amazon DynamoDB to store financial data in a custom indexing system. Use an AWS Lambda function to query relevant records based on input questions. Use Amazon SageMaker AI to generate responses.

Answer(s): A

Explanation:

Graph RAG is purpose-built for questions that require multi-hop reasoning across highly interconnected entities, and Amazon Neptune Analytics provides managed graph storage and traversal optimized for relationship queries. Using Amazon Bedrock Knowledge Bases with Graph RAG minimizes custom orchestration and relationship-mapping code while enabling fast, holistic retrieval of indirect relationships so the model can generate comprehensive answers within a low-latency target.



A healthcare company uses Amazon Bedrock to deploy an application that generates summaries of clinical documents. The application experiences inconsistent response quality with occasional factual hallucinations. Monthly costs exceed the company's projections by 40%. A GenAI developer must implement a near real-time monitoring solution to detect hallucinations, identify abnormal token consumption, and provide early warnings of cost anomalies. The solution must require minimal custom development work and maintenance overhead.

Which solution will meet these requirements?

  1. Configure Amazon CloudWatch alarms to monitor InputTokenCount and OutputTokenCount metrics to
    detect anomalies. Store model invocation logs in an Amazon S3 bucket. Use AWS Glue and Amazon Athena to identify potential hallucinations.
  2. Run Amazon Bedrock evaluation jobs that use LLM-based judgments to detect hallucinations. Configure Amazon CloudWatch to track token usage. Create an AWS Lambda function to process CloudWatch metrics. Configure the Lambda function to send usage pattern notifications.
  3. Configure Amazon Bedrock to store model invocation logs in an Amazon S3 bucket. Enable text output logging. Configure Amazon Bedrock guardrails to run contextual grounding checks to detect hallucinations.
    Create Amazon CloudWatch anomaly detection alarms for token usage metrics.
  4. Use AWS CloudTrail to log all Amazon Bedrock API calls. Create a custom dashboard in Amazon QuickSight to visualize token usage patterns. Use Amazon SageMaker Model Monitor to detect quality drift in generated summaries.

Answer(s): C

Explanation:

Amazon Bedrock invocation logging to Amazon S3 provides the raw prompts and outputs for audit and investigation with minimal effort. Bedrock guardrails with contextual grounding checks provide managed hallucination detection signals close to real time without building a separate evaluation pipeline. CloudWatch anomaly detection alarms on token usage metrics automatically flag abnormal consumption and cost anomalies early without requiring custom processing code.



A company is building a generative AI (GenAI) application that produces content based on a variety of internal and external data sources. The company wants to ensure that the generated output is fully traceable. The application must support data source registration and enable metadata tagging to attribute content to its original source. The application must also maintain audit logs of data access and usage throughout the pipeline.

Which solution will meet these requirements?

  1. Use AWS Lake Formation to catalog data sources and control access. Apply metadata tags directly in Amazon S3. Use AWS CloudTrail to monitor API activity.
  2. Use AWS Glue Data Catalog to register and tag data sources. Use Amazon CloudWatch Logs to monitor access patterns and application behavior.
  3. Store data in Amazon S3 and use object tagging for attribution. Use AWS Glue Data Catalog to manage schema information. Use AWS CloudTrail to log access to S3 buckets.
  4. Use AWS Glue Data Catalog to register all data sources. Apply metadata tags to attribute data sources.
    Use AWS CloudTrail to log access and activity across services.

Answer(s): D

Explanation:

AWS Glue Data Catalog provides centralized data source registration and supports metadata tagging to attribute content back to its originating source. AWS CloudTrail records API activity across AWS services, creating audit logs of data access and usage throughout the pipeline to support end-to-end traceability.



A financial services company needs to build a document analysis system that uses Amazon Bedrock to process quarterly reports. The system must analyze financial data, perform sentiment analysis, and validate compliance across batches of reports. Each batch contains 5 reports. Each report requires multiple foundation model (FM) calls. The solution must finish the analysis within 10 seconds for each batch. Current sequential processing takes 45 seconds for each batch.

Which solution will meet these requirements?

  1. Use AWS Lambda functions with provisioned concurrency to process each analysis type sequentially.

    Configure the Lambda function timeouts to 10 seconds. Configure automatic retries with exponential backoff.
  2. Use AWS Step Functions with a Parallel state to invoke separate AWS Lambda functions for each analysis type simultaneously. Configure Amazon Bedrock client timeouts. Use Amazon CloudWatch metrics to track execution time and model inference latency.
  3. Create an Amazon SQS queue to buffer analysis requests. Deploy multiple AWS Lambda functions with reserved concurrency. Configure each Lambda function to process different aspects of each report sequentially and then combine the results.
  4. Deploy an Amazon ECS cluster that runs containers that process each report sequentially. Use a load balancer to distribute batch workloads. Configure an auto-scaling policy based on CPU utilization to handle demand fluctuations.

Answer(s): B

Explanation:

AWS Step Functions can run the different analysis types in parallel by using a Parallel state, so the multiple FM call chains for financial analysis, sentiment analysis, and compliance validation execute concurrently instead of sequentially. This parallelization is the most direct way to reduce end-to-end batch time from 45 seconds toward the 10-second requirement while keeping orchestration managed and requiring minimal custom concurrency handling.



A company is using Amazon Bedrock to build a customer-facing AI assistant to handle sensitive customer inquiries. The company must use defense-in-depth safety controls to block sophisticated prompt injection attacks. The company must keep audit logs of all safety interventions. The AI assistant must have cross- Region failover capabilities.

Which solution will meet these requirements?

  1. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.
  2. Configure Amazon Bedrock guardrails to use content filters to protect against prompt injection attacks. Set the content filters to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls for audits.
  3. Deploy Amazon Comprehend custom classification to detect prompt injection attacks. Use Amazon API Gateway to validate requests. Use Amazon CloudWatch Logs with custom metrics to capture detailed intervention events.
  4. Configure Amazon Bedrock guardrails to use custom content filters to protect against harmful content. Set the content filters to high. Use word filters to protect against known attack patterns. Configure cross-Region guardrail replication to provide failover capabilities. Store logs in AWS CloudTrail for compliance auditing.

Answer(s): B

Explanation:

Bedrock guardrails provide an in-model safety layer to block harmful or unsafe inputs and outputs, and using AWS WAF adds an external control at the edge to help stop sophisticated prompt injection attempts before they reach the model, providing defense in depth. AWS CloudTrail supplies immutable audit logs of guardrail- related API activity and interventions for compliance reporting, and Bedrock can be invoked in a secondary Region for failover by routing traffic to an alternate endpoint when needed.



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?

  1. Use Amazon Bedrock with provisioned throughput to host the versions of the model. 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 the metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds.
  2. 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 to new model versions. Use Amazon CloudWatch to monitor performance metrics. Use external logic to adjust traffic between model versions and to roll back if performance falls below thresholds.
  3. Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions. Use variant weights to shift traffic. Use Amazon CloudWatch to monitor performance metrics. Use SageMaker Model Monitor to trigger AWS Lambda functions to roll back a model deployment if performance drops below a specified threshold. Configure an Amazon EventBridge rule to roll back model deployments if an anomaly is detected.
  4. 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 the inference logs.

Answer(s): A

Explanation:

A Step Functions workflow triggered by EventBridge can automate staged traffic shifts, wait windows, and continuous health checks by reading real-time CloudWatch inference latency and error metrics. The workflow can automatically advance traffic when metrics are healthy and initiate an immediate rollback when thresholds are breached, meeting the requirement for fully automated halt and revert without manual intervention.



A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput.

The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application:

python response = bedrock_runtime.invoke_model(modelId="anthropic.claude-v2", body=json.dumps(payload))

The company needs the application to use the provisioned throughput and to resolve the throttling issues.

Which solution will meet these requirements?

  1. Increase the number of model units (MUs) in the provisioned throughput configuration.
  2. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
  3. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
  4. Modify the application to use the InvokeModelWithResponseStream API instead of the InvokeModel API.

Answer(s): B

Explanation:

Provisioned throughput is consumed only when the application invokes the provisioned model resource, not the base on-demand model ID string. Using the ARN returned by CreateProvisionedModelThroughput ensures requests are routed to the purchased provisioned capacity, which prevents on-demand throttling while the provisioned capacity remains idle.



A company is building an AI advisory application by using Amazon Bedrock. The application will provide recommendations to customers. The company needs the application to explain its reasoning process and cite specific sources for data. The application must retrieve information from company data sources and show step- by-step reasoning for recommendations. The application must also link data claims to source documents and maintain response latency under 3 seconds.

Which solution will meet these requirements with the LEAST operational overhead?

  1. Use Amazon Bedrock Knowledge Bases with source attribution enabled. Use the Anthropic Claude Messages API with RAG to set high-relevance thresholds for source documents. Store reasoning and citations in Amazon S3 for auditing purposes.
  2. Use Amazon Bedrock with Anthropic Claude models and extended thinking. Configure a 4,000-token thinking budget. Store reasoning traces and citations in Amazon DynamoDB for auditing purposes.
  3. Configure Amazon SageMaker AI with a custom Anthropic Claude model. Use the model's reasoning parameter and AWS Lambda to process responses. Add source citations from a separate Amazon RDS database.
  4. Use Amazon Bedrock with Anthropic Claude models and chain-of-thought reasoning. Configure custom retrieval tracking with the Amazon Bedrock Knowledge Bases API. Use Amazon CloudWatch to monitor response latency metrics.

Answer(s): A

Explanation:

Amazon Bedrock Knowledge Bases provides managed retrieval from company data sources with built-in source attribution, so the application can link claims back to specific documents with minimal custom plumbing.
Using Claude with RAG lets the application generate a structured explanation of how the retrieved evidence supports the recommendation while keeping the retrieval-and-generation workflow managed and low-latency compared to building custom retrieval tracking or hosting models yourself.



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