A finance company is developing an AI assistant to help clients plan investments and manage their portfolios. The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company's approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Choose three.)
AAdd the high-risk conversation patterns to a denied topics guardrail.
BConfigure a content filter guardrail to filter prompts that contain the high-risk conversation patterns.
CConfigure a content filter guardrail to filter prompts that contain competitor names.
DAdd the names of competitors as custom word filters. Set the input and output actions to block.
ESet a low grounding score threshold.
FSet a high grounding score threshold.
A healthcare company is using Amazon Bedrock to build a Retrieval Augmented Generation (RAG) application that helps practitioners make clinical decisions. The application must achieve high accuracy for patient information retrievals, identify hallucinations in generated content, and reduce human review costs.
Which solution will meet these requirements?
AUse Amazon Comprehend to analyze and classify RAG responses and to extract medical entities and relationships. Use AWS Step Functions to orchestrate automated evaluations. Configure Amazon CloudWatch metrics to track entity recognition confidence scores. Configure CloudWatch to send an alert when accuracy falls below specified thresholds.
BImplement automated large language model (LLM)-based evaluations that use a specialized model that is fine-tuned for medical content to assess all responses. Deploy AWS Lambda functions to parallelize evaluations. Publish results to Amazon CloudWatch metrics that track relevance and factual accuracy.
CConfigure Amazon CloudWatch Synthetics to generate test queries that have known answers on a regular schedule, and track model success rates. Set up dashboards that compare synthetic test results against expected outcomes.
DDeploy a hybrid evaluation system that uses an automated LLM-as-a-judge evaluation to initially screen responses and targeted human reviews for edge cases. Use Amazon SageMaker Feature Store to maintain evaluation datasets. Use a built-in Amazon Bedrock evaluation to track retrieval precision and hallucination rates.
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?
AConfigure 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.
BConfigure 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.
CImplement direct frontend client connections to Amazon Bedrock by using IAM user credentials and the InvokeModelWithResponseStream API without any intermediate gateway or proxy layer.
DConfigure 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.
A media company must use Amazon Bedrock to implement a robust governance process for AI-generated content. The company needs to manage hundreds of prompt templates. Multiple teams use the templates across multiple AWS Regions to generate content. The solution must provide version control with approval workflows that include notifications for pending reviews. The solution must also provide detailed audit trails that document prompt activities and consistent prompt parameterization to enforce quality standards.
Which solution will meet these requirements?
AConfigure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch to create dashboards that display prompt usage metrics. Store the approval status of content in Amazon DynamoDB. Use AWS Lambda functions to enforce approvals.
BUse Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use IAM policies to control approval permissions. Create parameterized prompt templates by specifying variables.
CUse AWS Step Functions to create an approval workflow. Store prompts as documents in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.
DDeploy Amazon SageMaker Canvas with prompt templates that are stored in Amazon S3. Use AWS CloudFormation to implement version control. Use AWS Config to enforce approval policies.
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? (Choose two.)
AUse 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.
BCreate 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.
CCreate 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.
DCreate 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.
ECreate 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.
A company is developing an internal generative AI (GenAI) assistant that uses Amazon Bedrock to summarize corporate documents for multiple business units. The GenAI assistant must generate responses in a consistent format that includes a document summary, classification of business risks, and terms that are flagged for review. The GenAI assistant must adapt the tone of responses for each user's business unit, such as legal, human resources, or finance. The GenAI assistant must block hate speech, inappropriate topics, and sensitive information such as personal health information.
The company needs a solution to centrally manage prompt variants across business units and teams. The company wants to minimize ongoing orchestration efforts and maintenance for post-processing logic. The company also wants to have the ability to adjust content moderation criteria for the GenAI assistant over time.
Which solution will meet these requirements with the LEAST maintenance overhead?
AUse Amazon Bedrock Prompt Management to configure reusable templates and business unit-specific prompt variants. Apply Amazon Bedrock guardrails that have category filters and sensitive term lists to block prohibited content.
BUse Amazon Bedrock Prompt Management to define base templates. Enforce business unit-specific tone by using system prompt variables. Configure Amazon Bedrock guardrails to apply audience-based threshold tuning. Manage the guardrails by using an internal administration API.
CUse Amazon Bedrock with business unit-based instruction injection in API calls. Store response formatting rules in Amazon DynamoDB. Use AWS Step functions to validate responses. Use Amazon Comprehend to apply content filters after the GenAI assistant generates responses.
DUse Amazon Bedrock with custom prompt templates that are stored in Amazon DynamoDB. Create one AWS Lambda function to select business unit-specific prompts. Create a second Lambda function to call Amazon Comprehend to filter prohibited content from responses.
A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarities to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must be able to search documents that are in English, Spanish, and Portuguese. The application must filter documents by metadata such as publication date, regulatory agency, and document type.
The database stores approximately 10 million document embeddings. To minimize operational overhead, the company wants a solution that minimizes management and maintenance effort. The application must provide low-latency responses for real-time customer interactions.
Which solution will meet these requirements?
AUse Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering. Connect to Amazon Bedrock Knowledge Bases to enable Retrieval Augmented Generation (RAG) capabilities that use an Anthropic Claude foundation model (FM).
BDeploy an Amazon Aurora PostgreSQL database with the pgvector extension. Define tables to store embeddings and metadata. Use SQL queries to perform similarity searches. Send retrieved documents to Amazon Bedrock to generate responses.
CUse Amazon S3 Vectors to configure a vector index and non-filterable metadata fields. Integrate S3 Vectors with Amazon Bedrock to enable Retrieval Augmented Generation (RAG) capabilities.
DSet up an Amazon Neptune Analytics graph database. Configure a vector index that has appropriate dimensionality to store document embeddings. Use Amazon Bedrock to perform graph-based retrieval and to generate responses.
A medical company is building a generative AI (GenAI) application that uses RAG to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end user latency, even as the document collection grows to millions of documents.
Which solution will meet these requirements with the LEAST operational overhead?
AConfigure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.
BIncrease the dimensions of the vector embeddings from 384 to 1536. Use a post-processing AWS Lambda function to filter out irrelevant results after retrieval.
CReplace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.
DImplement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model that is hosted on Amazon SageMaker AI.
A company runs a generative AI (GenAI)-powered summarization application in an application AWS account that uses Amazon Bedrock. The application architecture includes an Amazon API Gateway REST API that forwards requests to AWS Lambda functions that are attached to private VPC subnets. The application summarizes sensitive customer records that the company stores in a governed data lake in a centralized data storage account. The company has enabled Amazon S3, Amazon Athena, and AWS Glue in the data storage account.
The company must ensure that calls that the application makes to Amazon Bedrock use only private connectivity between the company's application VPC and Amazon Bedrock. The company's data lake must provide fine-grained column-level access across the company's AWS accounts.
Which solution will meet these requirements?
AIn the application account, create interface VPC endpoints for Amazon Bedrock runtimes. Run Lambda functions in private subnets. Use IAM conditions on inference and data-plane policies to allow calls only to approved endpoints and roles. In the data storage account, use AWS Lake Formation LF-tag-based access control to create table and column-level cross-account grants.
BRun Lambda functions in private subnets. Configure a NAT gateway to provide access to Amazon Bedrock and the data lake. Use S3 bucket policies and ACLs to manage permissions. Export AWS CloudTrail logs to Amazon S3 to perform weekly reviews.
CCreate a gateway endpoint only for Amazon S3 in the application account. Invoke Amazon Bedrock through public endpoints. Use database-level grants in AWS Lake Formation to manage data access. Stream AWS CloudTrail logs to Amazon CloudWatch Logs. Do not set up metric filters or alarms.
DUse VPC endpoints to provide access to Amazon Bedrock and Amazon S3 in the application account. Use only IAM path-based policies to manage data lake access. Send AWS CloudTrail logs to Amazon CloudWatch Logs. Periodically create dashboards and allow public fallback for cross-Region reads to reduce setup time.
A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches.
Which solution will meet these requirements with the LEAST custom development effort?
AConfigure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.
BCreate the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails that have content filters to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension that use InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
CSet up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.
DCreate an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants. Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.
A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type.
Application users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing.
The company needs a solution to fix these performance issues and enhance the user experience.
Which solution will meet these requirements?
AUse AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
BIncrease the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
CUpdate the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
DChange the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?
ADeploy 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.
BDeploy 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.
CConfigure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
DUse 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.
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?
AUse 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.
BUse 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.
CUse 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.
DUse 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.
An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company's elevator technicians. The company uses Amazon Kinesis Data Streams to collect the elevator sensor data.
New regulatory rules require that a human technician must review all AI-generated recommendations. The company needs to establish human oversight workflows to review and approve AI recommendations. The company must store all human technician review decisions for audit purposes.
Which solution will meet these requirements?
ACreate a custom approval workflow by using AWS Lambda functions and Amazon SQS queues for human review of AI recommendations. Store all review decisions in Amazon DynamoDB for audit purposes.
BCreate an AWS Step Functions workflow that has a human approval step that uses the waitForTaskToken API to pause execution. After a human technician completes a review, use an AWS Lambda function to call the SendTaskSuccess API that has the approval decision. Store all review decisions in Amazon DynamoDB.
CCreate an AWS Glue workflow that has a human approval step. After the human technician review, integrate the application with an AWS Lambda function that calls the SendTaskSuccess API. Store all human technician review decisions in Amazon DynamoDB.
DConfigure Amazon EventBridge rules with custom event patterns to route AI recommendations to human technicians for review. Create AWS Glue jobs to process human technician approval queues. Use Amazon ElastiCache to cache all human technician review decisions.
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?
AIncrease the number of model units (MUs) in the provisioned throughput configuration.
BReplace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
CAdd exponential backoff retry logic to handle throttling exceptions during peak hours.
DModify the application to use the InvokeModelWithResponseStream API instead of the InvokeModel API.
A financial services company uses multiple foundation models (FMs) through Amazon Bedrock for its generative AI (GenAI) applications. To comply with a new regulation for GenAI use with sensitive financial data, the company needs a token management solution.
The token management solution must proactively alert when applications approach model-specific token limits. The solution must also process more than 5,000 requests each minute and maintain token usage metrics to allocate costs across business units.
Which solution will meet these requirements?
ADevelop model-specific tokenizers in an AWS Lambda function. Configure the Lambda function to estimate token usage before sending requests to Amazon Bedrock. Configure the Lambda function to publish metrics to Amazon CloudWatch and trigger alarms when requests approach thresholds. Store detailed token usage in Amazon DynamoDB to report costs.
BImplement Amazon Bedrock Guardrails with token quota policies. Capture metrics on rejected requests. Configure Amazon EventBridge rules to trigger notifications based on Amazon Bedrock Guardrails metrics. Use Amazon CloudWatch dashboards to visualize token usage trends across models.
CDeploy an Amazon SQS dead-letter queue for failed requests. Configure an AWS Lambda function to analyze token-related failures. Use Amazon CloudWatch Logs Insights to generate reports on token usage patterns based on error logs from Amazon Bedrock API responses.
DUse Amazon API Gateway to create a proxy for all Amazon Bedrock API calls. Configure request throttling based on custom usage plans with predefined token quotas. Configure API Gateway to reject requests that will exceed token limits.
A retail company is developing a customer service application that must process 10,000 daily queries about products, orders, and warranties. The application must be able to respond to queries about 50,000 product documents that are updated every day. The application must integrate with an order management API to check the status of orders and to help process returns. The application must maintain context throughout multi-turn interactions with customers. The company must collect complete audit trails for application responses.
Which solution will meet these requirements with the LEAST operational overhead?
ADeploy a fine-tuned Amazon Bedrock Anthropic Claude model for each product category. Create AWS Lambda functions to connect each model to the order management API. Store conversation history in Amazon DynamoDB.
BCreate a custom model that uses continued pre-training on Amazon Bedrock to handle all product documentation. Set up an Amazon API Gateway REST API that uses AWS Lambda functions to connect the model to the order management API.
CUse Amazon SageMaker AI with containers to deploy models. Use Amazon Kendra to search product documents. Use AWS Step Functions to orchestrate calls to the order management API.
DUse an Amazon Bedrock agent with action groups to integrate with the order management API. Associate an Amazon Bedrock knowledge base with the agent to search product documentation by using Retrieval Augmentation Generation (RAG). Enable trace events to capture audit trails.
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 a PostgresQL database.
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?
AMigrate 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, feature, and location. Create Amazon API Gateway HTTP API endpoints to transform user queries into structured search parameters.
BMigrate 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.
CKeep 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.
DMigrate 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.
A company uses Amazon Bedrock to generate technical content for customers. The company has recently experienced a surge in hallucination 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? (Choose two.)
AImplement zero-shot chain-of-thought (CoT) instructions that require step-by-step reasoning with explicit fact verification before the model generates each summary.
BUse Retrieval Augmented Generation (RAG) with an Amazon Bedrock knowledge base. Apply semantic chunking and tuned embeddings to ground summaries in source content.
CConfigure Amazon Bedrock guardrails to block any generated output that matches patterns that are associated with hallucinated content.
DIncrease the temperature parameter in Amazon Bedrock.
EPrompt the Amazon Bedrock model to summarize each full document in one pass.
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?
AUse AWS Lake Formation to catalog data sources and control access. Apply metadata tags directly in Amazon S3. Use AWS CloudTrail to monitor API activity.
BUse AWS Glue Data Catalog to register and tag data sources. Use Amazon CloudWatch Logs to monitor access patterns and application behavior.
CStore 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.
DUse 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.
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?
AUse 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.
BUse 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.
CUse 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.
DUse 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.
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 of the recommended products are not available for sale on the website or are not relevant to the customer. Customers also report that the solutions 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 solutions recommends products that are not in the company's product catalog. The company must resolve these issues.
Which solution will meet this requirement?
AIncrease grounding within Amazon Bedrock Guardrails. Enable Automated Reasoning checks. Set up provisioned throughput.
BUse 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.
CCreate an Amazon Bedrock knowledge base. Implement Retrieval Augmented Generation (RAG). Set the PerformanceConfigLatency parameter to optimized.
DStore product catalog data in Amazon OpenSearch Service. Validate the model's product recommendations against the product catalog. Use Amazon DynamoDB to implement response caching.
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?
AConfigure 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.
BConfigure 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.
CDeploy 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.
DConfigure 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.
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 pre-defined monthly compute budget and must maintain auto scaling capabilities.
Which solution will meet these requirements?
ADeploy a large, complex reasoning model on Amazon Bedrock. Purchase provisioned throughput and optimize for batch processing.
BDeploy a low-latency, real-time optimized model on Amazon Bedrock. Purchase provisioned throughput and set up automatic scaling policies.
CDeploy a large language model (LLM) on an Amazon SageMaker AI real-time endpoint that uses dedicated GPU instances.
DDeploy a mid-sized language model on an Amazon SageMaker AI serverless endpoint that is optimized for batch processing.
A company has a recommendation system. The system's applications run on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations.
The system is experiencing intermittent issues. Some recommendations do not match customer preferences. The company needs an observability solution to monitor operational metrics and detect patterns of operational performance degradation compared to established baselines. The solution must also generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns.
Which solution will meet these requirements?
AConfigure Amazon CloudWatch Container Insights for the application infrastructure. Set up CloudWatch alarms for latency thresholds. Add custom metrics for token counts by using the CloudWatch embedded metric format. Create CloudWatch dashboards to visualize the data.
BImplement AWS X-Ray to trace requests through the application components. Enable CloudWatch Logs Insights for error pattern detection. Set up AWS CloudTrail to monitor all API calls to Amazon Bedrock. Create custom dashboards in Amazon QuickSight.
CEnable Amazon CloudWatch Application Insights for the application resources. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segments. Configure CloudWatch anomaly detection on the model metrics. Establish log pattern analysis by using CloudWatch Logs Insights.
DUse Amazon OpenSearch Service with the Observability plugin. Ingest model metrics and logs by using Amazon Kinesis. Create custom Piped Processing Language (PPL) queries to analyze model behavior patterns. Establish operational dashboards to visualize anomalies in real time.
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