Winter Sale Limited Time 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: 65pass65

Free AWS Certified Generative AI Developer - Professional Practice Questions

Question # 11

A company has set up Amazon Q Developer Pro licenses for all developers at the company. The company maintains a list of approved resources that developers must use when developing applications. The approved resources include internal libraries, proprietary algorithmic techniques, and sample code with approved styling.

A new team of developers is using Amazon Q Developer to develop a new Java-based application. The company must ensure that the new developer team uses the company’s approved resources. The company does not want to make project-level modifications.

Which solution will meet these requirements?

Options:

A.  

Create a Git repository that contains all of the approved internal libraries, algorithms, and code samples. Include this Git repository in the application project locally as part of the workspace. Ensure that the developers use the workspace context to retrieve suggestions from the Git repository.

B.  

In the project root folder, create a folder named amazonq/rules. Add the approved internal libraries, algorithms, and code samples to the folder.

C.  

Create a folder in the application project named rules. Store the guidelines and code in the folder for Amazon Q Developer to reference for code suggestions.

D.  

Create an Amazon Q Developer customization that includes the approved data sources. Ensure that the developers use the customization to develop the application.

Discussion 0
Question # 12

A healthcare company is developing a document management system that stores medical research papers in an Amazon S3 bucket. The company needs a comprehensive metadata framework to improve search precision for a GenAI application. The metadata must include document timestamps, author information, and research domain classifications.

The solution must maintain a consistent metadata structure across all uploaded documents and allow foundation models (FMs) to understand document context without accessing full content.

Which solution will meet these requirements?

Options:

A.  

Store document timestamps in Amazon S3 system metadata. Use S3 object tags for domain classification. Implement custom user-defined metadata to store author information.

B.  

Set up S3 Object Lock with legal holds to track document timestamps. Use S3 object tags for author information. Implement S3 access points for domain classification.

C.  

Use S3 Inventory reports to track timestamps. Create S3 access points for domain classification. Store author information in S3 Storage Lens dashboards.

D.  

Use custom user-defined metadata to store author information. Use S3 Object Lock retention periods for timestamps. Use S3 Event Notifications for domain classification.

Discussion 0
Question # 13

A company purchases Amazon Q Developer Pro subscriptions for 500 developers to improve code quality and productivity. The company needs to create an observability system that tracks adoption metrics across the company. The observability system must be able to identify active subscription users compared to underused subscriptions. The system must give the company the ability to recognize power users every quarter and to identify teams that require additional training. The system must provide visibility into usage patterns such as the number of lines of Amazon Q generated code that each user has accepted. Which solution will meet these requirements?

Options:

A.  

Create a usage dashboard for Amazon Q Developer. Use the usage dashboard to track aggregated usage adoption metrics.

B.  

Use the Amazon Q Developer built-in administrator dashboard to track user adoption metrics across the company’s organization in AWS Organizations.

C.  

Collect user-level metrics in Amazon Q Developer. Store the metrics in an Amazon S3 bucket. Use Amazon QuickSight to visualize the usage data. Create dashboards to show adoption metrics for users and teams.

D.  

Configure AWS CloudTrail to track all Amazon Q Developer API calls in the company’s organization in AWS Organizations. Use an AWS Lambda function to process the logs. Store the processed logs in Amazon DynamoD

B.  

Create custom dashboards in Amazon Managed Grafana to visualize the data.

Discussion 0
Question # 14

A company is building a generative AI (GenAI) application that processes financial reports and provides summaries for analysts. The application must run two compute environments. In one environment, AWS Lambda functions must use the Python SDK to analyze reports on demand. In the second environment, Amazon EKS containers must use the JavaScript SDK to batch process multiple reports on a schedule. The application must maintain conversational context throughout multi-turn interactions, use the same foundation model (FM) across environments, and ensure consistent authentication.

Which solution will meet these requirements?

Options:

A.  

Use the Amazon Bedrock InvokeModel API with a separate authentication method for each environment. Store conversation states in Amazon DynamoD

B.  

Use custom I/O formatting logic for each programming language.

B.  

Use the Amazon Bedrock Converse API directly in both environments with a common authentication mechanism that uses IAM roles. Store conversation states in Amazon ElastiCache. Create programming language-specific wrappers for model parameters.

C.  

Create a centralized Amazon API Gateway REST API endpoint that handles all model interactions by using the InvokeModel API. Store interaction history in application process memory in each Lambda function or EKS container. Use environment variables to configure model parameters.

D.  

Use the Amazon Bedrock Converse API and IAM roles for authentication. Pass previous messages in the request messages array to maintain conversational context. Use programming language-specific SDKs to establish consistent API interfaces.

Discussion 0
Question # 15

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 # 16

A healthcare company is deploying an AI system that uses a foundation model (FM) to help clinicians make diagnostic decisions. The company’s ethics board requires the AI system to demonstrate fairness across patient demographic groups and comply with medical AI governance policies. During initial testing, the AI system provides recommendations without clear explanations or decision tracing. Clinicians are unable to review how the AI system produces diagnostic conclusions.

The company needs to implement a solution that provides transparent reasoning for AI outputs, enables systematic fairness testing, and ensures policy compliance for responsible AI use in healthcare settings. The solution must balance comprehensive explainability with real-time performance requirements. The solution must support rapid iteration for bias testing across multiple demographic variables. The solution must integrate seamlessly with existing clinical workflows while maintaining strict data privacy controls. The solution must handle complex medical and regulatory terminology.

Which solution will meet these requirements?

Options:

A.  

Use Amazon SageMaker Clarify to generate model explanations. Use Amazon Augmented AI (Amazon A2I) to implement human review workflows. Use AWS Config to enforce compliance policies across the AI system.

B.  

Use Amazon Comprehend Medical to analyze medical terminology. Use Amazon Textract to process documents. Use AWS CloudFormation to standardize deployment configurations.

C.  

Use Amazon Bedrock agent tracing to provide reasoning traces. Use Amazon Bedrock Prompt Management with A/B testing to perform fairness evaluations. Use Amazon Bedrock Guardrails to ensure policy compliance.

D.  

Use Amazon CloudWatch to collect performance metrics. Use Amazon EventBridge to trigger compliance checks. Use AWS Lambda functions to generate custom explanation reports.

Discussion 0
Question # 17

A healthcare company wants to develop a proof-of-concept application that uses Amazon Bedrock to automatically summarize medical documents. The company has 3 weeks to validate the application ' s accuracy. The application must comply with the company’s data privacy policies. The application must include metrics to evaluate summarization accuracy and processing time. Which solution will meet these requirements?

Options:

A.  

Create a dataset that includes 50-100 anonymized patient records. Implement Retrieval Augmented Generation (RAG) with a secure knowledge base. Use a judge model to evaluate accuracy metrics across three foundation models (FMs).

B.  

Fine-tune a single foundation model (FM) on patient records. Deploy the FM on Amazon Bedrock. Use Amazon Bedrock AgentCore to configure the FM as an agent. Conduct user testing on 500 company staff members.

C.  

Select the most powerful available AWS foundation model (FM). Create a chat interface by using Converse APIs. Test the application on 50-100 actual patient records by using only qualitative feedback from stakeholders. Use a custom web interface to gather real-world performance metrics.

D.  

Use the Strands SDK to deploy multiple agents that connect to multiple knowledge bases that contain specialized medical documents. Compare the responses of the agents. Evaluate the integration of the agents with the company ' s existing systems.

Discussion 0
Question # 18

A company is building a meeting analysis solution for its executive team. The solution uses AWS generative AI services. The solution must extract speaker-attributed content from recorded meetings, analyze visual elements from presentation slides, and create searchable summaries that link speaker comments to relevant visual context.

The solution must process 200 hours of meeting recordings each week. The solution must maintain data privacy by processing all meeting data within the AWS Cloud. The solution must store the source data for future retrieval and must be able to perform full-text searches.

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

Options:

A.  

Use Amazon Transcribe speaker diarization to process audio from the meeting recordings and to create speaker-attributed transcripts. Send video frames to Amazon Rekognition to perform image analysis. Use an AWS Lambda function to process outputs from Amazon Transcribe and Amazon Rekognition to generate searchable summaries that are stored in Amazon OpenSearch Service.

B.  

Use Anthropic Claude Sonnet in Amazon Bedrock to process the meeting recordings by using multimodal capabilities to analyze both audio transcripts and video frames. Use Amazon Transcribe to identify speakers in meeting recordings. Store the linked data in Amazon OpenSearch Service.

C.  

Use Amazon Bedrock to process meeting recordings. Use the Bedrock Data Automation (BDA) feature to extract audio streams. Define a custom output for the audio stream. Use Amazon Transcribe speaker diarization to transcribe recordings and identify speakers. Use Amazon Rekognition to analyze video frames. Store the output in Amazon DynamoD

B.  

Use Amazon Bedrock to generate summaries that link speakers to visual elements.

D.  

Use Amazon Transcribe to extract speaker-attributed content from meeting recordings. Use Anthropic Claude Sonnet in Amazon Bedrock to process the transcripts and video frames. Store the synchronized results in Amazon DynamoD

B.  

Use a custom indexing scheme to enable rapid retrieval.

Discussion 0
Question # 19

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?

Options:

A.  

Implement 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.

B.  

Implement 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.

C.  

Use 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 missing chunks from the point of failure. Maintain a small in-memory buffer o

D.  

Set Amazon Bedrock client request timeouts to 30 seconds. Implement client-side load shedding. Buffer partial results and stop new requests when application performance degrades. Set static token usage caps for all requests. Configure exponential backoff retries, dynamic chunk sizing, and context-aware token limits.

Discussion 0
Question # 20

A company uses Amazon Bedrock to generate technical content for customers. The company has recently experienced a surge in hallucinated outputs when the company’s model generates summaries of long technical documents. The model outputs include inaccurate or fabricated details. The company’s current solution uses a large foundation model (FM) with a basic one-shot prompt that includes the full document in a single input.

The company needs a solution that will reduce hallucinations and meet factual accuracy goals. The solution must process more than 1,000 documents each hour and deliver summaries within 3 seconds for each document.

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

Options:

A.  

Implement zero-shot chain-of-thought (CoT) instructions that require step-by-step reasoning with explicit fact verification before the model generates each summary.

B.  

Use Retrieval Augmented Generation (RAG) with an Amazon Bedrock knowledge base. Apply semantic chunking and tuned embeddings to ground summaries in source content.

C.  

Configure Amazon Bedrock guardrails to block any generated output that matches patterns that are associated with hallucinated content.

D.  

Increase the temperature parameter in Amazon Bedrock.

E.  

Prompt the Amazon Bedrock model to summarize each full document in one pass.

Discussion 0

AIP-C01 PDF

AIP-C01 PDF (Printable)
$54.25
$154.99

AIP-C01 Testing Engine

AIP-C01 PDF (Printable)
$59.5
$169.99

AIP-C01 PDF + Testing Engine

AIP-C01 PDF (Printable)
$74.55
$212.99

Free Exams Sample Questions