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AWS Certified Generative AI Developer - Professional Question and Answers

AWS Certified Generative AI Developer - Professional

Last Update May 31, 2026
Total Questions : 119

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Questions 1

A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms. The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system ' s suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies. Which solution will meet these requirements?

Options:

A.  

Use the Amazon Transcribe streaming API with standard settings to convert speech to text. Use Amazon Bedrock batch processing to perform inference. Store call recordings and metadata in Amazon S3. Use S3 Lifecycle policies to manage the storage.

B.  

Use the Amazon Transcribe streaming API with 100-ms audio chunks to optimize latency for the voice assistant. Call the Amazon Bedrock InvokeModelWithResponseStream operation to process client inquiries in real time. Store supervisor ratings in an Amazon DynamoDB table.

C.  

Use Amazon Transcribe batch processing to perform post-call analysis. Configure AWS Lambda functions to generate responses by using the Amazon Bedrock InvokeModel operation. Use Amazon CloudWatch to log supervisor feedback.

D.  

Use Amazon Transcribe to convert speech to text and to perform real-time analytics. Use Amazon Comprehend to perform sentiment analysis. Use Amazon SQS to queue processing tasks. Run the Amazon Bedrock InvokeModel operation to generate responses.

Discussion 0
Questions 2

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
Questions 3

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
Questions 4

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
Questions 5

A financial services company is creating a Retrieval Augmented Generation (RAG) application that uses Amazon Bedrock to generate summaries of market activities. The application relies on a vector database that stores a small proprietary dataset with a low index count. The application must perform similarity searches. The Amazon Bedrock model’s responses must maximize accuracy and maintain high performance.

The company needs to configure the vector database and integrate it with the application.

Which solution will meet these requirements?

Options:

A.  

Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. Configure a horizontal scaling policy based on performance metrics.

B.  

Launch an Amazon MemoryDB cluster and configure the index by using the Hierarchical Navigable Small World (HNSW) algorithm. Configure a vertical scaling policy based on performance metrics.

C.  

Launch an Amazon Aurora PostgreSQL cluster and configure the index by using the Inverted File with Flat Compression (IVFFlat) algorithm. Configure the instance class to scale to a larger size when the load increases.

D.  

Launch an Amazon DocumentDB cluster that has an IVFFlat index and a high probe value. Configure connections to the cluster as a replica set. Distribute reads to replica instances.

Discussion 0
Questions 6

A company deploys multiple Amazon Bedrock–based generative AI (GenAI) applications across multiple business units for customer service, content generation, and document analysis. Some applications show unpredictable token consumption patterns. The company requires a comprehensive observability solution that provides real-time visibility into token usage patterns across multiple models. The observability solution must support custom dashboards for multiple stakeholder groups and provide alerting capabilities for token consumption across all the foundation models that the company’s applications use.

Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)

Options:

A.  

Use Amazon CloudWatch metrics as data sources to create custom Amazon QuickSight dashboards that show token usage trends and usage patterns across FMs.

B.  

Use CloudWatch Logs Insights to analyze Amazon Bedrock invocation logs for token consumption patterns and usage attribution by application. Create custom queries to identify high-usage scenarios. Add log widgets to dashboards to enable continuous monitoring.

C.  

Create custom Amazon CloudWatch dashboards that combine native Amazon Bedrock token and invocation CloudWatch metrics. Set up CloudWatch alarms to monitor token usage thresholds.

D.  

Create dashboards that show token usage trends and patterns across the company’s FMs by using an Amazon Bedrock zero-ETL integration with Amazon Managed Grafana.

E.  

Implement Amazon EventBridge rules to capture Amazon Bedrock model invocation events. Route token usage data to Amazon OpenSearch Serverless by using Amazon Data Firehose. Use OpenSearch dashboards to analyze usage patterns.

Discussion 0
Questions 7

A company is developing a customer communication platform that uses an AI assistant powered by an Amazon Bedrock foundation model (FM). The AI assistant summarizes customer messages and generates initial response drafts.

The company wants to use Amazon Comprehend to implement layered content filtering. The layered content filtering must prevent sharing of offensive content, protect customer privacy, and detect potential inappropriate advice solicitation. Inappropriate advice solicitation includes requests for unethical practices, harmful activities, or manipulative behaviors.

The solution must maintain acceptable overall response times, so all pre-processing filters must finish before the content reaches the FM.

Which solution will meet these requirements?

Options:

A.  

Use parallel processing with asynchronous API calls. Use toxicity detection for offensive content. Use prompt safety classification for inappropriate advice solicitation. Use personally identifiable information (PII) detection without redaction.

B.  

Use custom classification to build an FM that detects offensive content and inappropriate advice solicitation. Apply personally identifiable information (PII) detection as a secondary filter only when messages pass the custom classifier.

C.  

Deploy a multi-stage process. Configure the process to use prompt safety classification first, then toxicity detection on safe prompts only, and finally personally identifiable information (PII) detection in streaming mode. Route flagged messages through Amazon EventBridge for human review.

D.  

Use toxicity detection with thresholds configured to 0.5 for all categories. Use parallel processing for both prompt safety classification and personally identifiable information (PII) detection with entity redaction. Apply Amazon CloudWatch alarms to filter metrics.

Discussion 0
Questions 8

A company is building a multicloud generative AI (GenAI)-powered secret resolution application that uses Amazon Bedrock and Agent Squad. The application resolves secrets from multiple sources, including key stores and hardware security modules (HSMs). The application uses AWS Lambda functions to retrieve secrets from the sources. The application uses AWS AppConfig to implement dynamic feature gating. The application supports secret chaining and detects secret drift. The application handles short-lived and expiring secrets. The application also supports prompt flows for templated instructions. The application uses AWS Step Functions to orchestrate agents to resolve the secrets and to manage secret validation and drift detection.

The company finds multiple issues during application testing. The application does not refresh expired secrets in time for agents to use. The application sends alerts for secret drift, but agents still use stale data. Prompt flows within the application reuse outdated templates, which cause cascading failures. The company must resolve the performance issues.

Which solution will meet this requirement?

Options:

A.  

Use Step Functions Map states to run agent workflows in parallel. Pass updated secret metadata through Lambda function outputs. Use AWS AppConfig to version all prompt flows to gate and roll back faulty templates.

B.  

Use Amazon Bedrock Agents only. Configure Amazon Bedrock guardrails to restrict prompt variation. Use an inline JSON schema for a single agent’s workflow definition to chain tool calls.

C.  

Use a centralized Amazon EventBridge pipeline to invoke each agent. Store intermediate prompts in Amazon DynamoDB. Resolve agent ordering by using TTL-based backoff and retries.

D.  

Use Amazon EventBridge Pipes to invoke resolvers based on Amazon CloudWatch log patterns. Store response metadata in DynamoDB with TTL and versioned writes. Use Amazon Q Developer to dynamically generate fallback prompts.

Discussion 0
Questions 9

A financial services company needs to pre-process unstructured data such as customer transcripts, financial reports, and documentation. The company stores the unstructured data in Amazon S3 to support an Amazon Bedrock application.

The company must validate data quality, create auditable metadata, monitor data metrics, and customize text chunking to optimize foundation model (FM) performance.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.  

Use Amazon SageMaker Data Wrangler to create a data flow. Configure Amazon CloudWatch metrics and alarms to monitor data quality. Use a custom AWS Lambda function to pre-process the data. Load processed data into Amazon Bedrock.

B.  

Set up an AWS Glue crawler to catalog data sources. Create AWS Glue ETL jobs to run custom transformation scripts. Use AWS Glue Data Quality to validate and monitor data quality. Load processed data into Amazon Bedrock.

C.  

Use Amazon Comprehend to extract entities. Create an AWS Lambda function to chunk text. Run Amazon Athena to query and validate data quality. Load processed data into Amazon Bedrock.

D.  

Create an AWS Step Functions workflow to orchestrate data pre-processing tasks. Run custom code on Amazon EC2 instances. Use Amazon SageMaker Model Monitor to monitor data quality. Load processed data into Amazon Bedrock.

Discussion 0
Questions 10

A company is using Amazon Bedrock to design an application to help researchers apply for grants. The application is based on an Amazon Nova Pro foundation model (FM). The application contains four required inputs and must provide responses in a consistent text format. The company wants to receive a notification in Amazon Bedrock if a response contains bullying language. However, the company does not want to block all flagged responses.

The company creates an Amazon Bedrock flow that takes an input prompt and sends it to the Amazon Nova Pro FM. The Amazon Nova Pro FM provides a response.

Which additional steps must the company take to meet these requirements? (Select TWO.)

Options:

A.  

Use Amazon Bedrock Prompt Management to specify the required inputs as variables. Select an Amazon Nova Pro FM. Specify the output format for the response. Add the prompt to the prompts node of the flow.

B.  

Create an Amazon Bedrock guardrail that applies the hate content filter. Set the filter response to block. Add the guardrail to the prompts node of the flow.

C.  

Create an Amazon Bedrock prompt router. Specify an Amazon Nova Pro FM. Add the required inputs as variables to the input node of the flow. Add the prompt router to the prompts node. Add the output format to the output node.

D.  

Create an Amazon Bedrock guardrail that applies the insults content filter. Set the filter response to detect. Add the guardrail to the prompts node of the flow.

E.  

Create an Amazon Bedrock application inference profile that specifies an Amazon Nova Pro FM. Specify the output format for the response in the description. Include a tag for each of the input variables. Add the profile to the prompts node of the flow.

Discussion 0
Questions 11

A company provides a service that helps users from around the world discover new restaurants. The service has 50 million monthly active users. The company wants to implement a semantic search solution across a database that contains 20 million restaurants and 200 million reviews. The company currently stores the data in PostgreSQL.

The solution must support complex natural language queries and return results for at least 95% of queries within 500 ms. The solution must maintain data freshness for restaurant details that update hourly. The solution must also scale cost-effectively during peak usage periods.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.  

Migrate the restaurant data to Amazon OpenSearch Service. Implement keyword-based search rules that use custom analyzers and relevance tuning to find restaurants based on attributes such as cuisine type, features, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.

B.  

Migrate the restaurant data to Amazon OpenSearch Service. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant descriptions, reviews, and menu items. When users submit natural language queries, convert the queries to embeddings by using the same FM. Perform k-nearest neighbors (k-NN) searches to find semantically similar results.

C.  

Keep the restaurant data in PostgreSQL and implement a pgvector extension. Use a foundation model (FM) in Amazon Bedrock to generate vector embeddings from restaurant data. Store the vector embeddings directly in PostgreSQL. Create an AWS Lambda function to convert natural language queries to vector representations by using the same FM. Configure the Lambda function to perform similarity searches within the database.

D.  

Migrate restaurant data to an Amazon Bedrock knowledge base by using a custom ingestion pipeline. Configure the knowledge base to automatically generate embeddings from restaurant information. Use the Amazon Bedrock Retrieve API with built-in vector search capabilities to query the knowledge base directly by using natural language input.

Discussion 0
Questions 12

A company developed a multimodal content analysis application by using Amazon Bedrock. The application routes different content types (text, images, and code) to specialized foundation models (FMs).

The application needs to handle multiple types of routing decisions. Simple routing based on file extension must have minimal latency. Complex routing based on content semantics requires analysis before FM selection. The application must provide detailed history and support fallback options when primary FMs fail.

Which solution will meet these requirements?

Options:

A.  

Configure AWS Lambda functions that call Amazon Bedrock FMs for all routing logic. Use conditional statements to determine the appropriate FM based on content type and semantics.

B.  

Create a hybrid solution. Handle simple routing based on file extensions in application code. Handle complex content-based routing by using an AWS Step Functions state machine with JSONata for content analysis and the InvokeModel API for specialized FMs.

C.  

Deploy separate AWS Step Functions workflows for each content type with routing logic in AWS Lambda functions. Use Amazon EventBridge to coordinate between workflows when fallback to alternate FMs is required.

D.  

Use Amazon SQS with different SQS queues for each content type. Configure AWS Lambda consumers that analyze content and invoke appropriate FMs based on message attributes by using Amazon Bedrock with an AWS SDK.

Discussion 0
Questions 13

A company is developing a generative AI (GenAI) application that uses Amazon Bedrock foundation models. The application has several custom tool integrations. The application has experienced unexpected token consumption surges despite consistent user traffic.

The company needs a solution that uses Amazon Bedrock model invocation logging to monitor InputTokenCount and OutputTokenCount metrics. The solution must detect unusual patterns in tool usage and identify which specific tool integrations cause abnormal token consumption. The solution must also automatically adjust thresholds as traffic patterns change.

Which solution will meet these requirements?

Options:

A.  

Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch dashboards for token metrics. Configure static CloudWatch alarms with fixed thresholds for each tool integration.

B.  

Store model invocation logs in Amazon S3. Use AWS Glue and Amazon Athena to analyze token usage trends.

C.  

Use Amazon CloudWatch Logs to capture model invocation logs. Create CloudWatch metric filters to extract tool-specific invocation patterns. Apply CloudWatch anomaly detection alarms that automatically adjust baselines for each tool’s token metrics.

D.  

Store model invocation logs in an Amazon S3 bucket. Use AWS Lambda to process logs in real time. Manually update CloudWatch alarm thresholds based on trends identified by the Lambda function.

Discussion 0
Questions 14

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?

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.

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.

Discussion 0
Questions 15

A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company ' s engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company ' s marketing team tracks business metrics such as conversion rates and revenue impact in its own systems. The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics. Which solution will meet these requirements?

Options:

A.  

Create CloudWatch dashboards that include technical metrics and imported business metrics. Configure CloudWatch composite alarms that combine technical data and business data. Use Amazon SNS to set up notifications to stakeholders.

B.  

Use Amazon Managed Grafana to visualize technical metrics from CloudWatch with business metrics from external sources. Configure Amazon Managed Grafana alerts to invoke AWS Lambda functions. Configure the Lambda functions to remediate issues automatically when metrics exceed predefined thresholds.

C.  

Stream CloudWatch metrics to Amazon S3 by using CloudWatch metric streams. Create Amazon QuickSight dashboards to visualize the combined technical metrics and business metrics. Set up Amazon EventBridge rules to send notifications to stakeholders when metrics exceed predefined thresholds.

D.  

Configure CloudWatch custom dashboards that integrate operational metrics with imported business metrics. Set up CloudWatch composite alarms with anomaly detection. Use Amazon SNS to create alarm actions to notify stakeholders when correlated metrics indicate performance issues.

Discussion 0
Questions 16

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?

Options:

A.  

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.

B.  

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.

C.  

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.

D.  

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.

Discussion 0
Questions 17

An ecommerce company is developing a generative AI application that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solution takes a long time to generate some recommendations.

The company investigates the issues and finds that most interactions between customers and the product recommendation solution are unique. The company confirms that the solution recommends products that are not in the company’s product catalog. The company must resolve these issues.

Which solution will meet this requirement?

Options:

A.  

Increase grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput.

B.  

Use prompt engineering to restrict the model responses to relevant products. Use streaming techniques such as the InvokeModelWithResponseStream action to reduce perceived latency for the customers.

C.  

Create an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation RAG. Set the PerformanceConfigLatency parameter to optimized.

D.  

Store product catalog data in Amazon OpenSearch Service. Validate the model’s product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.

Discussion 0
Questions 18

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
Questions 19

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?

Options:

A.  

Use 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.

B.  

Use 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.

C.  

Use 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.

D.  

Use 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.

Discussion 0
Questions 20

An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) 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?

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.  

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.

C.  

Configure an AWS Lambda function to fetch routing configuration 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.

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.

Discussion 0
Questions 21

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?

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.

Discussion 0
Questions 22

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:

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?

Options:

A.  

Increase the number of model units (MUs) in the provisioned throughput configuration.

B.  

Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.

C.  

Add exponential backoff retry logic to handle throttling exceptions during peak hours.

D.  

Modify the application to use the invokeModelWithResponseStream API instead of the invokeModel API.

Discussion 0
Questions 23

A company is planning to deploy multiple generative AI (GenAI) applications to five independent business units that operate in multiple countries in Europe and the Americas. Each application uses Amazon Bedrock Retrieval Augmented Generation (RAG) patterns with business unit-specific knowledge bases that store terabytes of unstructured data.

The company must establish well-architected, standardized components for security controls, observability practices, and deployment patterns across all the GenAI applications. The components must be reusable, versioned, and governed consistently.

Which solution will meet these requirements?

Options:

A.  

Configure Amazon API Gateway REST API endpoints for the GenAI applications. Deploy common security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens in standardized AWS CloudFormation templates. Use CloudFormation Guard after deployment to validate policy compliance in each business unit.

B.  

Create standardized AWS CloudFormation templates to implement security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Establish a centralized repository for version control. Integrate a CI/CD pipeline with CloudFormation Guard to enforce consistent and repeatable deployments across business units.

C.  

Use AWS Service Catalog to define standardized portfolios and versioned products for each business unit. Use the portfolios to enforce security, observability, and RAG patterns based on the AWS Well-Architected Generative AI Lens. Require business units to use the Service Catalog console to deploy resources.

D.  

Document security controls, observability requirements, and RAG patterns based on the AWS Well-Architected Generative AI Lens in a shared design document. Use Amazon Macie to enforce deployment. Delegate implementation responsibility to each business unit.

Discussion 0
Questions 24

A company is developing a customer support application that uses Amazon Bedrock foundation models (FMs) to provide real-time AI assistance to the company’s employees. The application must display AI-generated responses character by character as the responses are generated. The application needs to support thousands of concurrent users with minimal latency. The responses typically take 15 to 45 seconds to finish.

Which solution will meet these requirements?

Options:

A.  

Configure an Amazon API Gateway WebSocket API with an AWS Lambda integration. Configure the WebSocket API to invoke the Amazon Bedrock InvokeModelWithResponseStream API and stream partial responses through WebSocket connections.

B.  

Configure an Amazon API Gateway REST API with an AWS Lambda integration. Configure the REST API to invoke the Amazon Bedrock standard InvokeModel API and implement frontend client-side polling every 100 ms for complete response chunks.

C.  

Implement direct frontend client connections to Amazon Bedrock by using IAM user credentials and the InvokeModelWithResponseStream API without any intermediate gateway or proxy layer.

D.  

Configure an Amazon API Gateway HTTP API with an AWS Lambda integration. Configure the HTTP API to cache complete responses in an Amazon DynamoDB table and serve the responses through multiple paginated GET requests to frontend clients.

Discussion 0
Questions 25

A company uses Amazon Bedrock to build a Retrieval Augmented Generation (RAG) system. The RAG system uses an Amazon Bedrock Knowledge Bases that is based on an Amazon S3 bucket as the data source for emergency news video content. The system retrieves transcripts, archived reports, and related documents from the S3 bucket.

The RAG system uses state-of-the-art embedding models and a high-performing retrieval setup. However, users report slow responses and irrelevant results, which cause decreased user satisfaction. The company notices that vector searches are evaluating too many documents across too many content types and over long periods of time.

The company determines that the underlying models will not benefit from additional fine-tuning. The company must improve retrieval accuracy by applying smarter constraints and wants a solution that requires minimal changes to the existing architecture.

Which solution will meet these requirements?

Options:

A.  

Enhance embeddings by using a domain-adapted model that is specifically trained on emergency news content for improved vector similarity.

B.  

Migrate to Amazon OpenSearch Service. Use vector fields and metadata filters to define the scope of results retrieval.

C.  

Enable metadata-aware filtering within the Amazon Bedrock knowledge base by indexing S3 object metadata.

D.  

Migrate to an Amazon Q Business index to perform structured metadata filtering and document categorization during retrieval.

Discussion 0
Questions 26

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

Discussion 0
Questions 27

A bank is building a generative AI (GenAI) application that uses Amazon Bedrock to assess loan applications by using scanned financial documents. The application must extract structured data from the documents. The application must redact personally identifiable information (PII) before inference. The application must use foundation models (FMs) to generate approvals. The application must route low-confidence document extraction results to human reviewers who are within the same AWS Region as the loan applicant.

The company must ensure that the application complies with strict Regional data residency and auditability requirements. The application must be able to scale to handle 25,000 applications each day and provide 99.9% availability.

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

Options:

A.  

Deploy Amazon Textract and Amazon Augmented AI within the same Region to extract relevant data from the scanned documents. Route low-confidence pages to human reviewers.

B.  

Use AWS Lambda functions to detect and redact PII from submitted documents before inference. Apply Amazon Bedrock guardrails to prevent inappropriate or unauthorized content in model outputs. Configure Region-specific IAM roles to enforce data residency requirements and to control access to the extracted data.

C.  

Use Amazon Kendra and Amazon OpenSearch Service to extract field-level values semantically from the uploaded documents before inference.

D.  

Store uploaded documents in Amazon S3 and apply object metadata. Configure IAM policies to store original documents within the same Region as each applicant. Enable object tagging for future audits.

E.  

Use AWS Glue Data Quality to validate the structured document data. Use AWS Step Functions to orchestrate a review workflow that includes a prompt engineering step that transforms validated data into optimized prompts before invoking Amazon Bedrock to assess loan applications.

F.  

Use Amazon SageMaker Clarify to generate fairness and bias reports based on model scoring decisions that Amazon Bedrock makes.

Discussion 0
Questions 28

A pharmaceutical company is developing a Retrieval Augmented Generation 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
Questions 29

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
Questions 30

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
Questions 31

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?

Options:

A.  

Create 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.

B.  

Create 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.

C.  

Create 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 that has a deployment stage for each environment that uses AWS CodeBuild deploy actions.

D.  

Create 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 applica

Discussion 0
Questions 32

A company is developing a generative AI (GenAI) application that analyzes customer service calls in real time and generates suggested responses for human customer service agents. The application must process 500,000 concurrent calls during peak hours with less than 200 ms end-to-end latency for each suggestion. The company uses existing architecture to transcribe customer call audio streams. The application must not exceed a predefined monthly compute budget and must maintain auto scaling capabilities.

Which solution will meet these requirements?

Options:

A.  

Deploy a large, complex reasoning model on Amazon Bedrock. Purchase provisioned throughput and optimize for batch processing.

B.  

Deploy a low-latency, real-time optimized model on Amazon Bedrock. Purchase provisioned throughput and set up automatic scaling policies.

C.  

Deploy a large language model (LLM) on an Amazon SageMaker real-time endpoint that uses dedicated GPU instances.

D.  

Deploy a mid-sized language model on an Amazon SageMaker serverless endpoint that is optimized for batch processing.

Discussion 0
Questions 33

A company is developing a generative AI (GenAI)-powered customer support application that uses Amazon Bedrock foundation models (FMs). The application must maintain conversational context across multiple interactions with the same user. The application must run clarification workflows to handle ambiguous user queries. The company must store encrypted records of each user conversation to use for personalization. The application must be able to handle thousands of concurrent users while responding to each user quickly.

Which solution will meet these requirements?

Options:

A.  

Use an AWS Step Functions Express workflow to orchestrate conversation flow. Invoke AWS Lambda functions to run clarification logic. Store conversation history in Amazon RDS and use session IDs as the primary key.

B.  

Use an AWS Step Functions Standard workflow to orchestrate clarification workflows. Include Wait for a Callback patterns to manage the workflows. Store conversation history in Amazon DynamoD

B.  

Purchase on-demand capacity and configure server-side encryption.

C.  

Deploy the application by using an Amazon API Gateway REST API to route user requests to an AWS Lambda function to update and retrieve conversation context. Store conversation history in Amazon S3 and configure server-side encryption. Save each interaction as a separate JSON file.

D.  

Use AWS Lambda functions to call Amazon Bedrock inference APIs. Use Amazon SQS queues to orchestrate clarification steps. Store conversation history in an Amazon ElastiCache (Redis OSS) cluster. Configure encryption at rest.

Discussion 0
Questions 34

An ecommerce company is developing a generative AI (GenAI) solution that uses Amazon Bedrock with Anthropic Claude to recommend products to customers. Customers report that some recommended products are not available for sale or are not relevant. Customers also report long response times for some recommendations.

The company confirms that most customer interactions are unique and that the solution recommends products not present in the product catalog.

Which solution will meet this requirement?

Options:

A.  

Increase grounding within Amazon Bedrock Guardrails. Enable automated reasoning checks. Set up provisioned throughput.

B.  

Use prompt engineering to restrict model responses to relevant products. Use streaming inference to reduce perceived latency.

C.  

Create an Amazon Bedrock Knowledge Bases and implement Retrieval Augmented Generation (RAG). Set the PerformanceConfigLatency parameter to optimized.

D.  

Store product catalog data in Amazon OpenSearch Service. Validate model recommendations against the catalog. Use Amazon DynamoDB for response caching.

Discussion 0
Questions 35

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 DynamoDB. 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