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AIP-C01 Practice Questions

AWS Certified Generative AI Developer - Professional

Last Update 15 hours ago
Total Questions : 119

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Question # 21

A retail company runs an application that makes product recommendations to customers on the company’s website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear in the responses, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement?

Options:

A.  

Use an Amazon Bedrock Guardrails filter on the prompt. Set the input filter strength to HIGH.

B.  

Generate multiple response variants that include the promotional message in different ways. Use a reranker model to select the most coherent version based on relevance to the original prompt.

C.  

Run the prompt through Amazon Bedrock. Process the response through Amazon Bedrock AgentCore to add the promotional message. Rerank the results by using the original prompt and the desired message as context.

D.  

Reinforce the requirement to include the new promotional message within product recommendations by using an output indicator in prompts to the FM.

Discussion 0
Question # 22

A medical company uses Amazon Bedrock to power a clinical documentation summarization system. The system produces inconsistent summaries when handling complex clinical documents. The system performed well on simple clinical documents.

The company needs a solution that diagnoses inconsistencies, compares prompt performance against established metrics, and maintains historical records of prompt versions.

Which solution will meet these requirements?

Options:

A.  

Create multiple prompt variants by using Prompt management in Amazon Bedrock. Manually test the prompts with simple clinical documents. Deploy the highest performing version by using the Amazon Bedrock console.

B.  

Implement version control for prompts in a code repository with a test suite that contains complex clinical documents and quantifiable evaluation metrics. Use an automated testing framework to compare prompt versions and document performance patterns.

C.  

Deploy each new prompt version to separate Amazon Bedrock API endpoints. Split production traffic between the endpoints. Configure Amazon CloudWatch to capture response metrics and user feedback for automatic version selection.

D.  

Create a custom prompt evaluation flow in Amazon Bedrock Flows that applies the same clinical document inputs to different prompt variants. Use Amazon Comprehend Medical to analyze and score the factual accuracy of each version.

Discussion 0
Question # 23

A company is designing a solution that uses foundation models (FMs) to support multiple AI workloads. Some FMs must be invoked on demand and in real time. Other FMs require consistent high-throughput access for batch processing.

The solution must support hybrid deployment patterns and run workloads across cloud infrastructure and on-premises infrastructure to comply with data residency and compliance requirements.

Which combination of steps will meet these requirements? (Select TWO.)

Options:

A.  

Use AWS Lambda to orchestrate low-latency FM inference by invoking FMs hosted on Amazon SageMaker AI asynchronous endpoints.

B.  

Configure provisioned throughput in Amazon Bedrock to ensure consistent performance for high-volume workloads.

C.  

Deploy FMs to Amazon SageMaker AI endpoints with support for edge deployment by using Amazon SageMaker Neo. Orchestrate the FMs by using AWS Lambda to support hybrid deployment.

D.  

Use Amazon Bedrock with auto-scaling to handle unpredictable traffic surges.

E.  

Use Amazon SageMaker JumpStart to host and invoke the FMs.

Discussion 0
Question # 24

A company is using Amazon Bedrock and Anthropic Claude 3 Haiku to develop an AI assistant. The AI assistant normally processes 10,000 requests each hour but experiences surges of up to 30,000 requests each hour during peak usage periods. The AI assistant must respond within 2 seconds while operating across multiple AWS Regions.

The company observes that during peak usage periods, the AI assistant experiences throughput bottlenecks that cause increased latency and occasional request timeouts. The company must resolve the performance issues.

Which solution will meet this requirement?

Options:

A.  

Purchase provisioned throughput and sufficient model units (MUs) in a single Region. Configure the application to retry failed requests with exponential backoff.

B.  

Implement token batching to reduce API overhead. Use cross-Region inference profiles to automatically distribute traffic across available Regions.

C.  

Set up auto scaling AWS Lambda functions in each Region. Implement client-side round-robin request distribution. Purchase one model unit (MU) of provisioned throughput as a backup.

D.  

Implement batch inference for all requests by using Amazon S3 buckets across multiple Regions. Use Amazon SQS to set up an asynchronous retrieval process.

Discussion 0
Question # 25

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. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds.

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

Options:

A.  

Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze multi-hop relationships between entities and automatically identify related information across documents.

B.  

Use Amazon Bedrock Knowledge Bases and an Amazon OpenSearch Service vector store to implement custom relationship identification logic that uses AWS Lambda to query multiple vector embeddings in sequence.

C.  

Use Amazon OpenSearch Serverless vector search with k-nearest neighbor (k-NN). Implement manual relationship mapping in an application layer that runs on Amazon EC2 Auto Scaling.

D.  

Use Amazon DynamoDB to store financial data in a custom indexing system. Use AWS Lambda to query relevant records. Use Amazon SageMaker to generate responses.

Discussion 0
Question # 26

A company has a customer service application that uses Amazon Bedrock to generate personalized responses to customer inquiries. The company needs to establish a quality assurance process to evaluate prompt effectiveness and model configurations across updates. The process must automatically compare outputs from multiple prompt templates, detect response quality issues, provide quantitative metrics, and allow human reviewers to give feedback on responses. The process must prevent configurations that do not meet a predefined quality threshold from being deployed.

Which solution will meet these requirements?

Options:

A.  

Create an AWS Lambda function that sends sample customer inquiries to multiple Amazon Bedrock model configurations and stores responses in Amazon S3. Use Amazon QuickSight to visualize response patterns. Manually review outputs daily. Use AWS CodePipeline to deploy configurations that meet the quality threshold.

B.  

Use Amazon Bedrock evaluation jobs to compare model outputs by using custom prompt datasets. Configure AWS CodePipeline to run the evaluation jobs when prompt templates change. Configure CodePipeline to deploy only configurations that exceed the predefined quality threshold.

C.  

Set up Amazon CloudWatch alarms to monitor response latency and error rates from Amazon Bedrock. Use Amazon EventBridge rules to notify teams when thresholds are exceeded. Configure a manual approval workflow in AWS Systems Manager.

D.  

Use AWS Lambda functions to create an automated testing framework that samples production traffic and routes duplicate requests to the updated model version. Use Amazon Comprehend sentiment analysis to compare results. Block deployment if sentiment scores decrease.

Discussion 0
Question # 27

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?

Options:

A.  

Store 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.

B.  

Store the active model provider in AWS AppConfig. Configure a Lambda function to read the configuration at runtime to determine which model to invoke.

C.  

Configure 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.

D.  

Store 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.

Discussion 0
Question # 28

A global healthcare company is deploying a GenAI application on Amazon Bedrock to produce treatment recommendations. Regulations vary for each country where the company operates. Some countries require the company to retain all model inputs and outputs for 2 years. Other countries require the company to submit data for local audits only. Medical providers require consistent medical terminology across all locations. However, the treatment recommendations that the model produces must adapt to local patient demographics. The solution must also integrate with existing electronic health record (EHR) systems. The application must support up to 10,000 healthcare provider queries every day with sub-second response times. The company must be able to review the application before deployments and approve of prompt changes. The application must produce comprehensive logs for prompts, responses, and user context. Which solution will meet these requirements?

Options:

A.  

Use AWS CloudTrail to log API calls. Create standard prompts in Amazon Bedrock Prompt Management that include variables for patient demographics. Implement IAM policies to ensure that only approves users can access prompts.

B.  

Use Amazon CloudWatch Logs to collect detailed model invocation logs. Store the logs in Amazon S3. Create parameterized prompts in Amazon Bedrock Prompt Management that include variables for treatment options. Enable prompt versioning and set up an approval workflow.

C.  

Create AWS Lambda functions to dynamically generate prompts that enforce clinical language requirements. Use Amazon CloudWatch Logs to track model invocations. Use Amazon SQS queues to implement a prompt approval workflow.

D.  

Store prompt templates in Amazon S3. Use S3 Object Lock to implement version control. Use Amazon EventBridge to track model invocations. Use AWS Config to monitor changes to prompt templates.

Discussion 0
Question # 29

A medical company is creating a generative AI (GenAI) system by using Amazon Bedrock. The system processes data from various sources and must maintain end-to-end data lineage. The system must also use real-time personally identifiable information (PII) filtering and audit trails to automatically report compliance.

Which solution will meet these requirements?

Options:

A.  

Use AWS Glue Data Catalog to register all data sources and track lineage. Use Amazon Bedrock Guardrails PII filters. Enable AWS CloudTrail logging for all Amazon Bedrock API calls with Amazon S3 integration. Use Amazon Macie to scan stored data for sensitive information and publish findings to Amazon CloudWatch Logs. Create CloudWatch dashboards to visualize the findings and generate automated compliance reports.

B.  

Use AWS Config to track data source configurations and changes. Use AWS WAF with custom rules to filter PII at the application layer before Amazon Bedrock processes the data. Configure Amazon EventBridge to capture and route audit events to Amazon S3. Use Amazon Comprehend Medical with scheduled AWS Lambda functions to analyze stored outputs for compliance violations.

C.  

Use AWS DataSync to replicate data sources to track lineage. Configure Amazon Macie to scan Amazon Bedrock outputs for sensitive information. Use AWS Systems Manager Session Manager to log user interactions. Deploy Amazon Textract with AWS Step Functions workflows to identify and redact PII from generated reports.

D.  

Configure Amazon Athena to query data sources to analyze and report on data lineage. Use Amazon CloudWatch custom metrics to monitor PII exposure in Amazon Bedrock responses and establish AWS X-Ray tracing to generate an audit trail. Use an Amazon Rekognition Custom Labels model to detect sensitive information in the data that Amazon Bedrock processes.

Discussion 0
Question # 30

A pharmaceutical company is developing a Retrieval Augmented Generation (RAG) application that uses an Amazon Bedrock knowledge base. The knowledge base uses Amazon OpenSearch Service as a data source for more than 25 million scientific papers. Users report that the application produces inconsistent answers that cite irrelevant sections of papers when queries span methodology, results, and discussion sections of the papers.

The company needs to improve the knowledge base to preserve semantic context across related paragraphs on the scale of the entire corpus of data.

Which solution will meet these requirements?

Options:

A.  

Configure the knowledge base to use fixed-size chunking. Set a 300-token maximum chunk size and a 10% overlap between chunks. Use an appropriate Amazon Bedrock embedding model.

B.  

Configure the knowledge base to use hierarchical chunking. Use parent chunks that contain 1,000 tokens and child chunks that contain 200 tokens. Set a 50-token overlap between chunks.

C.  

Configure the knowledge base to use semantic chunking. Use a buffer size of 1 and a breakpoint percentile threshold of 85% to determine chunk boundaries based on content meaning.

D.  

Configure the knowledge base not to use chunking. Manually split each document into separate files before ingestion. Apply post-processing reranking during retrieval.

Discussion 0
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