{"id":120796,"date":"2026-02-12T02:49:37","date_gmt":"2026-02-12T02:49:37","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=120796"},"modified":"2026-02-12T02:49:37","modified_gmt":"2026-02-12T02:49:37","slug":"new-aip-c01-dumps-v8-02-2026-your-proper-materials-for-aws-certified-generative-ai-developer-professional-exam-preparation","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/new-aip-c01-dumps-v8-02-2026-your-proper-materials-for-aws-certified-generative-ai-developer-professional-exam-preparation.html","title":{"rendered":"New AIP-C01 Dumps (V8.02) 2026 &#8211; Your Proper Materials for AWS Certified Generative AI Developer &#8211; Professional Exam Preparation"},"content":{"rendered":"<p>The AWS Certified Generative AI Developer &#8211; Professional (AIP-C01) is a professional-level AWS certification focused on building and putting real-world generative AI applications into production (not just prototypes), with emphasis on effective use of foundation models via AWS tools like <a href=\"https:\/\/www.dumpsbase.com\/amazon.html\"><em><strong>Amazon<\/strong><\/em><\/a> Bedrock, SageMaker, and related services. Preparing for the AIP-C01 exam requires access to the most current and reliable study materials, so you can come to DumpsBase and get new AIP-C01 dumps (V8.02) for 2026. Our comprehensive and accurate exam questions from AIP-C01 dumps (V8.02) are designed to strengthen your understanding of critical topics. Each question comes with detailed explanations that not only provide the correct answer but also clarify underlying concepts, helping you build a solid foundation in generative AI development on AWS. With DumpsBase&#8217;s updated AIP-C01 exam dumps (V8.02) and structured study approach, you can approach the certification exam with confidence, knowing you&#8217;ve covered all critical areas and are fully prepared to pass on your first attempt.<\/p>\n<h2>AWS certification <span style=\"background-color: #ffff99;\"><em>AIP-C01 free dumps are below<\/em><\/span> to help you check the quality:<\/h2>\n<script>\n\t  window.fbAsyncInit = function() {\n\t    FB.init({\n\t      appId            : '622169541470367',\n\t      autoLogAppEvents : true,\n\t      xfbml            : true,\n\t      version          : 'v3.1'\n\t    });\n\t  };\n\t\n\t  (function(d, s, id){\n\t     var js, fjs = d.getElementsByTagName(s)[0];\n\t     if (d.getElementById(id)) {return;}\n\t     js = d.createElement(s); js.id = id;\n\t     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n\t     fjs.parentNode.insertBefore(js, fjs);\n\t   }(document, 'script', 'facebook-jssdk'));\n\t<\/script><script type=\"text\/javascript\" >\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \nif(!window.jQuery) alert(\"The important jQuery library is not properly loaded in your site. Your WordPress theme is probably missing the essential wp_head() call. You can switch to another theme and you will see that the plugin works fine and this notice disappears. If you are still not sure what to do you can contact us for help.\");\n});\n<\/script>  \n  \n<div  id=\"watupro_quiz\" class=\"quiz-area single-page-quiz\">\n<p id=\"submittingExam11691\" style=\"display:none;text-align:center;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/img\/loading.gif\" width=\"16\" height=\"16\"><\/p>\n\n<div class=\"watupro-exam-description\" id=\"description-quiz-11691\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-11691\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-458686'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A financial services company is building a customer support application that retrieves relevant financial regulation documents from a database based on semantic similarity to user queries. The application must integrate with Amazon Bedrock to generate responses. The application must search documents in English, Spanish, and Portuguese. The application must filter documents by metadata such as publication date, regulatory agency, and document type. <br \/>\r<br>The database stores approximately 10 million document embeddings. To minimize operational overhead, the company wants a solution that minimizes management and maintenance effort while providing low-latency responses for real-time customer interactions. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='458686' \/><input type='hidden' id='answerType458686' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458686[]' id='answer-id-1773095' class='answer   answerof-458686 ' value='1773095'   \/><label for='answer-id-1773095' id='answer-label-1773095' class=' answer'><span>Use Amazon OpenSearch Serverless to provide vector search capabilities and metadata filtering. Integrate with Amazon Bedrock Knowledge Bases to enable Retrieval Augmented Generation (RAG) using an Anthropic Claude foundation model.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458686[]' id='answer-id-1773096' class='answer   answerof-458686 ' value='1773096'   \/><label for='answer-id-1773096' id='answer-label-1773096' class=' answer'><span>Deploy an Amazon Aurora PostgreSQL database with the pgvector extension. Store embeddings and metadata in tables. Use SQL queries for similarity search and send results to Amazon Bedrock for response generation.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458686[]' id='answer-id-1773097' class='answer   answerof-458686 ' value='1773097'   \/><label for='answer-id-1773097' id='answer-label-1773097' class=' answer'><span>Use Amazon S3 Vectors to configure a vector index and non-filterable metadata fields. Integrate S3 Vectors with Amazon Bedrock for RA<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458686[]' id='answer-id-1773098' class='answer   answerof-458686 ' value='1773098'   \/><label for='answer-id-1773098' id='answer-label-1773098' class=' answer'><span>Set up an Amazon Neptune Analytics database with a vector index. Use graph-based retrieval and Amazon Bedrock for response generation.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-2' style=';'><div id='questionWrap-2'  class='   watupro-question-id-458687'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>A company deploys multiple Amazon BedrockCbased 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\u2019s applications use. <br \/>\r<br>Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)<\/div><input type='hidden' name='question_id[]' id='qID_2' value='458687' \/><input type='hidden' id='answerType458687' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458687[]' id='answer-id-1773099' class='answer   answerof-458687 ' value='1773099'   \/><label for='answer-id-1773099' id='answer-label-1773099' class=' answer'><span>Use Amazon CloudWatch metrics as data sources to create custom Amazon QuickSight dashboards that show token usage trends and usage patterns across FMs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458687[]' id='answer-id-1773100' class='answer   answerof-458687 ' value='1773100'   \/><label for='answer-id-1773100' id='answer-label-1773100' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458687[]' id='answer-id-1773101' class='answer   answerof-458687 ' value='1773101'   \/><label for='answer-id-1773101' id='answer-label-1773101' class=' answer'><span>Create custom Amazon CloudWatch dashboards that combine native Amazon Bedrock token and invocation CloudWatch metrics. Set up CloudWatch alarms to monitor token usage thresholds.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458687[]' id='answer-id-1773102' class='answer   answerof-458687 ' value='1773102'   \/><label for='answer-id-1773102' id='answer-label-1773102' class=' answer'><span>Create dashboards that show token usage trends and patterns across the company\u2019s FMs by using an Amazon Bedrock zero-ETL integration with Amazon Managed Grafana.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-458687[]' id='answer-id-1773103' class='answer   answerof-458687 ' value='1773103'   \/><label for='answer-id-1773103' id='answer-label-1773103' class=' answer'><span>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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-3' style=';'><div id='questionWrap-3'  class='   watupro-question-id-458688'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>A hotel company wants to enhance a legacy Java-based property management system (PMS) by adding AI capabilities. The company wants to use Amazon Bedrock Knowledge Bases to provide staff with room availability information and hotel-specific details. The solution must maintain separate access controls for each hotel that the company manages. The solution must provide room availability information in near real time and must maintain consistent performance during peak usage periods. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='458688' \/><input type='hidden' id='answerType458688' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458688[]' id='answer-id-1773104' class='answer   answerof-458688 ' value='1773104'   \/><label for='answer-id-1773104' id='answer-label-1773104' class=' answer'><span>Deploy a single Amazon Bedrock knowledge base that contains combined data for all hotels. Configure AWS Lambda functions to synchronize data from each hotel\u2019s PMS database through direct \r\nAPI connections. Implement AWS CloudTrail logging with hotel-specific filters to audit access logs for each hotel\u2019s data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458688[]' id='answer-id-1773105' class='answer   answerof-458688 ' value='1773105'   \/><label for='answer-id-1773105' id='answer-label-1773105' class=' answer'><span>Create an Amazon EventBridge rule for each hotel that is invoked by changes to the PMS database. Configure the rule to send updates to a centralized Amazon Bedrock knowledge base in a management AWS account. Configure resource-based policies to enforce hotel-specific access controls.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458688[]' id='answer-id-1773106' class='answer   answerof-458688 ' value='1773106'   \/><label for='answer-id-1773106' id='answer-label-1773106' class=' answer'><span>Implement one Amazon Bedrock knowledge base for each hotel in a multi-account structure. Use direct data ingestion to provide near real-time room availability information. Schedule regular synchronization for less critical information.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458688[]' id='answer-id-1773107' class='answer   answerof-458688 ' value='1773107'   \/><label for='answer-id-1773107' id='answer-label-1773107' class=' answer'><span>Build a centralized Amazon Bedrock Agents solution that uses multiple knowledge bases. Implement AWS IAM Identity Center with hotel-specific permission sets to control staff access.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-4' style=';'><div id='questionWrap-4'  class='   watupro-question-id-458689'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>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\u2019s responses must maximize accuracy and maintain high performance. <br \/>\r<br>The company needs to configure the vector database and integrate it with the application. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='458689' \/><input type='hidden' id='answerType458689' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458689[]' id='answer-id-1773108' class='answer   answerof-458689 ' value='1773108'   \/><label for='answer-id-1773108' id='answer-label-1773108' class=' answer'><span>Launch an Amazon MemoryDB cluster and configure the index by using the Flat algorithm. \r\nConfigure a horizontal scaling policy based on performance metrics.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458689[]' id='answer-id-1773109' class='answer   answerof-458689 ' value='1773109'   \/><label for='answer-id-1773109' id='answer-label-1773109' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458689[]' id='answer-id-1773110' class='answer   answerof-458689 ' value='1773110'   \/><label for='answer-id-1773110' id='answer-label-1773110' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458689[]' id='answer-id-1773111' class='answer   answerof-458689 ' value='1773111'   \/><label for='answer-id-1773111' id='answer-label-1773111' class=' answer'><span>Launch an Amazon DocumentDB cluster that has an IVFFlat index and a high probe value. \r\nConfigure connections to the cluster as a replica set. Distribute reads to replica instances.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-5' style=';'><div id='questionWrap-5'  class='   watupro-question-id-458690'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='458690' \/><input type='hidden' id='answerType458690' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458690[]' id='answer-id-1773112' class='answer   answerof-458690 ' value='1773112'   \/><label for='answer-id-1773112' id='answer-label-1773112' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458690[]' id='answer-id-1773113' class='answer   answerof-458690 ' value='1773113'   \/><label for='answer-id-1773113' id='answer-label-1773113' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458690[]' id='answer-id-1773114' class='answer   answerof-458690 ' value='1773114'   \/><label for='answer-id-1773114' id='answer-label-1773114' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458690[]' id='answer-id-1773115' class='answer   answerof-458690 ' value='1773115'   \/><label for='answer-id-1773115' id='answer-label-1773115' class=' answer'><span>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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-6' style=';'><div id='questionWrap-6'  class='   watupro-question-id-458691'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>An elevator service company has developed an AI assistant application by using Amazon Bedrock. The application generates elevator maintenance recommendations to support the company\u2019s elevator technicians. The company uses Amazon Kinesis Data Streams to collect the elevator sensor data. <br \/>\r<br>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='458691' \/><input type='hidden' id='answerType458691' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458691[]' id='answer-id-1773116' class='answer   answerof-458691 ' value='1773116'   \/><label for='answer-id-1773116' id='answer-label-1773116' class=' answer'><span>Create 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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458691[]' id='answer-id-1773117' class='answer   answerof-458691 ' value='1773117'   \/><label for='answer-id-1773117' id='answer-label-1773117' class=' answer'><span>Create 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 with the approval decision. Store all review decisions in Amazon DynamoD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458691[]' id='answer-id-1773118' class='answer   answerof-458691 ' value='1773118'   \/><label for='answer-id-1773118' id='answer-label-1773118' class=' answer'><span>Create 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 AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458691[]' id='answer-id-1773119' class='answer   answerof-458691 ' value='1773119'   \/><label for='answer-id-1773119' id='answer-label-1773119' class=' answer'><span>Store all human technician review decisions in Amazon DynamoD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458691[]' id='answer-id-1773120' class='answer   answerof-458691 ' value='1773120'   \/><label for='answer-id-1773120' id='answer-label-1773120' class=' answer'><span>Configure 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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-7' style=';'><div id='questionWrap-7'  class='   watupro-question-id-458692'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>A company uses an AI assistant application to summarize the company\u2019s website content and provide information to customers. The company plans to use Amazon Bedrock to give the application access to a foundation model (FM). <br \/>\r<br>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='458692' \/><input type='hidden' id='answerType458692' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458692[]' id='answer-id-1773121' class='answer   answerof-458692 ' value='1773121'   \/><label for='answer-id-1773121' id='answer-label-1773121' class=' answer'><span>Create one AWS CDK application. Create multiple pipelines in AWS CodePipeline. Configure each pipeline to have its own settings for each F<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458692[]' id='answer-id-1773122' class='answer   answerof-458692 ' value='1773122'   \/><label for='answer-id-1773122' id='answer-label-1773122' class=' answer'><span>Configure the application to invoke the Amazon Bedrock FMs by using the aws_bedrock.ProvisionedModel.fromProvisionedModelArn() method.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458692[]' id='answer-id-1773123' class='answer   answerof-458692 ' value='1773123'   \/><label for='answer-id-1773123' id='answer-label-1773123' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458692[]' id='answer-id-1773124' class='answer   answerof-458692 ' value='1773124'   \/><label for='answer-id-1773124' id='answer-label-1773124' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458692[]' id='answer-id-1773125' class='answer   answerof-458692 ' value='1773125'   \/><label for='answer-id-1773125' id='answer-label-1773125' class=' answer'><span>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 application code.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-8' style=';'><div id='questionWrap-8'  class='   watupro-question-id-458693'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>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. <br \/>\r<br>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_8' value='458693' \/><input type='hidden' id='answerType458693' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458693[]' id='answer-id-1773126' class='answer   answerof-458693 ' value='1773126'   \/><label for='answer-id-1773126' id='answer-label-1773126' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458693[]' id='answer-id-1773127' class='answer   answerof-458693 ' value='1773127'   \/><label for='answer-id-1773127' id='answer-label-1773127' class=' answer'><span>Store model invocation logs in Amazon S3. Use AWS Glue and Amazon Athena to analyze token usage trends.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458693[]' id='answer-id-1773128' class='answer   answerof-458693 ' value='1773128'   \/><label for='answer-id-1773128' id='answer-label-1773128' class=' answer'><span>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\u2019s token metrics.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458693[]' id='answer-id-1773129' class='answer   answerof-458693 ' value='1773129'   \/><label for='answer-id-1773129' id='answer-label-1773129' class=' answer'><span>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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-9' style=';'><div id='questionWrap-9'  class='   watupro-question-id-458694'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='458694' \/><input type='hidden' id='answerType458694' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458694[]' id='answer-id-1773130' class='answer   answerof-458694 ' value='1773130'   \/><label for='answer-id-1773130' id='answer-label-1773130' class=' answer'><span>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\u2019s MCP client to invoke \r\nthe MCP server asynchronously.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458694[]' id='answer-id-1773131' class='answer   answerof-458694 ' value='1773131'   \/><label for='answer-id-1773131' id='answer-label-1773131' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458694[]' id='answer-id-1773132' class='answer   answerof-458694 ' value='1773132'   \/><label for='answer-id-1773132' id='answer-label-1773132' class=' answer'><span>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 AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458694[]' id='answer-id-1773133' class='answer   answerof-458694 ' value='1773133'   \/><label for='answer-id-1773133' id='answer-label-1773133' class=' answer'><span>Use Amazon Cognito to enforce OAuth 2.1.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458694[]' id='answer-id-1773134' class='answer   answerof-458694 ' value='1773134'   \/><label for='answer-id-1773134' id='answer-label-1773134' class=' answer'><span>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\u2019s MCP configuration, specify the Lambda layer ARN as the command. Specify the user credentials as environment variables.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-10' style=';'><div id='questionWrap-10'  class='   watupro-question-id-458695'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>A company is using Amazon Bedrock and Anthropic Claude 3 Haiku to develop an AI assistant. The AI assistant normally processes 10,000 requests each hour but experiences surges of up to 30,000 requests each hour during peak usage periods. The AI assistant must respond within 2 seconds while operating across multiple AWS Regions. <br \/>\r<br>The company observes that during peak usage periods, the AI assistant experiences throughput bottlenecks that cause increased latency and occasional request timeouts. The company must <br \/>\r<br>resolve the performance issues. <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='458695' \/><input type='hidden' id='answerType458695' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458695[]' id='answer-id-1773135' class='answer   answerof-458695 ' value='1773135'   \/><label for='answer-id-1773135' id='answer-label-1773135' class=' answer'><span>Purchase provisioned throughput and sufficient model units (MUs) in a single Region. Configure the application to retry failed requests with exponential backoff.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458695[]' id='answer-id-1773136' class='answer   answerof-458695 ' value='1773136'   \/><label for='answer-id-1773136' id='answer-label-1773136' class=' answer'><span>Implement token batching to reduce API overhead. Use cross-Region inference profiles to automatically distribute traffic across available Regions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458695[]' id='answer-id-1773137' class='answer   answerof-458695 ' value='1773137'   \/><label for='answer-id-1773137' id='answer-label-1773137' class=' answer'><span>Set up auto scaling AWS Lambda functions in each Region. Implement client-side round-robin request distribution. Purchase one model unit (MU) of provisioned throughput as a backup.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458695[]' id='answer-id-1773138' class='answer   answerof-458695 ' value='1773138'   \/><label for='answer-id-1773138' id='answer-label-1773138' class=' answer'><span>Implement batch inference for all requests by using Amazon S3 buckets across multiple Regions. Use Amazon SQS to set up an asynchronous retrieval process.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-11' style=';'><div id='questionWrap-11'  class='   watupro-question-id-458696'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>A company uses AWS Lake Formation to set up a data lake that contains databases and tables for multiple business units across multiple AWS Regions. The company wants to use a foundation model (FM) through Amazon Bedrock to perform fraud detection. The FM must ingest sensitive financial data from the data lake. The data includes some customer personally identifiable information (PII). <br \/>\r<br>The company must design an access control solution that prevents PII from appearing in a production environment. The FM must access only authorized data subsets that have PII redacted from specific data columns. The company must capture audit trails for all data access. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='458696' \/><input type='hidden' id='answerType458696' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458696[]' id='answer-id-1773139' class='answer   answerof-458696 ' value='1773139'   \/><label for='answer-id-1773139' id='answer-label-1773139' class=' answer'><span>Create a separate dataset in a separate Amazon S3 bucket for each business unit and Region combination. Configure S3 bucket policies to control access based on IAM roles that are assigned to FM training instances. Use S3 access logs to track data access.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458696[]' id='answer-id-1773140' class='answer   answerof-458696 ' value='1773140'   \/><label for='answer-id-1773140' id='answer-label-1773140' class=' answer'><span>Configure the FM to authenticate by using AWS Identity and Access Management roles and Lake Formation permissions based on LF-Tag expressions. Define business units and Regions as LF-Tags that are assigned to databases and tables. Use AWS CloudTrail to collect comprehensive audit trails of data access.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458696[]' id='answer-id-1773141' class='answer   answerof-458696 ' value='1773141'   \/><label for='answer-id-1773141' id='answer-label-1773141' class=' answer'><span>Use direct IAM principal grants on specific databases and tables in Lake Formation. Create a custom application layer that logs access requests and further filters sensitive columns before sending data to the F<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458696[]' id='answer-id-1773142' class='answer   answerof-458696 ' value='1773142'   \/><label for='answer-id-1773142' id='answer-label-1773142' class=' answer'><span>Configure the FM to request temporary credentials from AWS Security Token Service. Access the data by using presigned S3 URLs that are generated by an API that applies business unit and Regional filters. Use AWS CloudTrail to collect comprehensive audit trails of data access.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-12' style=';'><div id='questionWrap-12'  class='   watupro-question-id-458697'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>A medical company is building a generative AI (GenAI) application that uses Retrieval Augmented Generation (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. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_12' value='458697' \/><input type='hidden' id='answerType458697' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458697[]' id='answer-id-1773143' class='answer   answerof-458697 ' value='1773143'   \/><label for='answer-id-1773143' id='answer-label-1773143' class=' answer'><span>Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458697[]' id='answer-id-1773144' class='answer   answerof-458697 ' value='1773144'   \/><label for='answer-id-1773144' id='answer-label-1773144' class=' answer'><span>Increase the dimensions of the vector embeddings from 384 to 1536. Use a post-processing AWS Lambda function to filter out irrelevant results after retrieval.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458697[]' id='answer-id-1773145' class='answer   answerof-458697 ' value='1773145'   \/><label for='answer-id-1773145' id='answer-label-1773145' class=' answer'><span>Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458697[]' id='answer-id-1773146' class='answer   answerof-458697 ' value='1773146'   \/><label for='answer-id-1773146' id='answer-label-1773146' class=' answer'><span>Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model hosted on Amazon SageMaker.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-13' style=';'><div id='questionWrap-13'  class='   watupro-question-id-458698'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='458698' \/><input type='hidden' id='answerType458698' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458698[]' id='answer-id-1773147' class='answer   answerof-458698 ' value='1773147'   \/><label for='answer-id-1773147' id='answer-label-1773147' class=' answer'><span>Deploy a large, complex reasoning model on Amazon Bedrock. Purchase provisioned throughput and optimize for batch processing.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458698[]' id='answer-id-1773148' class='answer   answerof-458698 ' value='1773148'   \/><label for='answer-id-1773148' id='answer-label-1773148' class=' answer'><span>Deploy a low-latency, real-time optimized model on Amazon Bedrock. Purchase provisioned throughput and set up automatic scaling policies.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458698[]' id='answer-id-1773149' class='answer   answerof-458698 ' value='1773149'   \/><label for='answer-id-1773149' id='answer-label-1773149' class=' answer'><span>Deploy a large language model (LLM) on an Amazon SageMaker real-time endpoint that uses \r\ndedicated GPU instances.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458698[]' id='answer-id-1773150' class='answer   answerof-458698 ' value='1773150'   \/><label for='answer-id-1773150' id='answer-label-1773150' class=' answer'><span>Deploy a mid-sized language model on an Amazon SageMaker serverless endpoint that is optimized for batch processing.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-14' style=';'><div id='questionWrap-14'  class='   watupro-question-id-458699'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='458699' \/><input type='hidden' id='answerType458699' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458699[]' id='answer-id-1773151' class='answer   answerof-458699 ' value='1773151'   \/><label for='answer-id-1773151' id='answer-label-1773151' class=' answer'><span>Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch dashboards to display prompt usage metrics. Store approval status in Amazon DynamoD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458699[]' id='answer-id-1773152' class='answer   answerof-458699 ' value='1773152'   \/><label for='answer-id-1773152' id='answer-label-1773152' class=' answer'><span>Use AWS Lambda functions to enforce approvals.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458699[]' id='answer-id-1773153' class='answer   answerof-458699 ' value='1773153'   \/><label for='answer-id-1773153' id='answer-label-1773153' class=' answer'><span>Use Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use AWS Identity and Access Management policies to control approval permissions. Create parameterized prompt templates by specifying variables.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458699[]' id='answer-id-1773154' class='answer   answerof-458699 ' value='1773154'   \/><label for='answer-id-1773154' id='answer-label-1773154' class=' answer'><span>Use AWS Step Functions to create an approval workflow. Store prompts in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458699[]' id='answer-id-1773155' class='answer   answerof-458699 ' value='1773155'   \/><label for='answer-id-1773155' id='answer-label-1773155' class=' answer'><span>Deploy Amazon SageMaker Canvas with prompt templates stored in Amazon S3. Use AWS CloudFormation for version control. Use AWS Config to enforce approval policies.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-15' style=';'><div id='questionWrap-15'  class='   watupro-question-id-458700'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze 50 to 200 pages of technical documents. Users are experiencing inconsistent responses and receiving truncated outputs when processing documents that exceed the FM's context window limits. <br \/>\r<br>Which solution will resolve this problem?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='458700' \/><input type='hidden' id='answerType458700' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458700[]' id='answer-id-1773156' class='answer   answerof-458700 ' value='1773156'   \/><label for='answer-id-1773156' id='answer-label-1773156' class=' answer'><span>Configure fixed-size chunking at 4,000 tokens for each chunk with 20% overlap. Use application-level logic to link multiple chunks sequentially until the FM's maximum context window of 200,000 tokens is reached before making inference calls.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458700[]' id='answer-id-1773157' class='answer   answerof-458700 ' value='1773157'   \/><label for='answer-id-1773157' id='answer-label-1773157' class=' answer'><span>Use hierarchical chunking with parent chunks of 8,000 tokens and child chunks of 2,000 tokens. Use Amazon Bedrock Knowledge Bases built-in retrieval to automatically select relevant parent chunks based on query context. Configure overlap tokens to maintain semantic continuity.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458700[]' id='answer-id-1773158' class='answer   answerof-458700 ' value='1773158'   \/><label for='answer-id-1773158' id='answer-label-1773158' class=' answer'><span>Use semantic chunking with a breakpoint percentile threshold of 95% and a buffer size of 3 sentences. Use the RetrieveAndGenerate API to dynamically select the most relevant chunks based on embedding similarity scores.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458700[]' id='answer-id-1773159' class='answer   answerof-458700 ' value='1773159'   \/><label for='answer-id-1773159' id='answer-label-1773159' class=' answer'><span>Create a pre-processing AWS Lambda function that analyzes document token count by using the FM's tokenizer. Configure the Lambda function to split documents into equal segments that fit within 80% of the context window. Configure the Lambda function to process each segment independently before aggregating the results.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-16' style=';'><div id='questionWrap-16'  class='   watupro-question-id-458701'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>An ecommerce company is using Amazon Bedrock to build a generative AI (GenAI) application. The application uses AWS Step Functions to orchestrate a multi-agent workflow to produce detailed product descriptions. The workflow consists of three sequential states: a description generator, a technical specifications validator, and a brand voice consistency checker. Each state produces intermediate reasoning traces and outputs that are passed to the next state. The application uses an Amazon S3 bucket for process storage and to store outputs. <br \/>\r<br>During testing, the company discovers that outputs between Step Functions states frequently exceed the 256 KB quota and cause workflow failures. A GenAI Developer needs to revise the application architecture to efficiently handle the Step Functions 256 KB quota and maintain workflow observability. The revised architecture must preserve the existing multi-agent reasoning and acting (ReAct) pattern. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='458701' \/><input type='hidden' id='answerType458701' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458701[]' id='answer-id-1773160' class='answer   answerof-458701 ' value='1773160'   \/><label for='answer-id-1773160' id='answer-label-1773160' class=' answer'><span>Store intermediate outputs in Amazon DynamoD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458701[]' id='answer-id-1773161' class='answer   answerof-458701 ' value='1773161'   \/><label for='answer-id-1773161' id='answer-label-1773161' class=' answer'><span>Pass only references between states. Create a Map state that retrieves the complete data from DynamoDB when required for each agent's processing step.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458701[]' id='answer-id-1773162' class='answer   answerof-458701 ' value='1773162'   \/><label for='answer-id-1773162' id='answer-label-1773162' class=' answer'><span>Configure an Amazon Bedrock integration to use the S3 bucket URI in the input parameters for large outputs. Use the ResultPath and ResultSelector fields to route S3 references between the agent steps while maintaining the sequential validation workflow.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458701[]' id='answer-id-1773163' class='answer   answerof-458701 ' value='1773163'   \/><label for='answer-id-1773163' id='answer-label-1773163' class=' answer'><span>Use AWS Lambda functions to compress outputs to less than 256 KB before each agent state. Configure each agent task to decompress outputs before processing and to compress results before passing them to the next state.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458701[]' id='answer-id-1773164' class='answer   answerof-458701 ' value='1773164'   \/><label for='answer-id-1773164' id='answer-label-1773164' class=' answer'><span>Configure a separate Step Functions state machine to handle each agent\u2019s processing. Use Amazon EventBridge to coordinate the execution flow between state machines. Use S3 references for the outputs as event data.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-17' style=';'><div id='questionWrap-17'  class='   watupro-question-id-458702'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>A company is using Amazon Bedrock to build a customer-facing AI assistant that handles 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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='458702' \/><input type='hidden' id='answerType458702' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458702[]' id='answer-id-1773165' class='answer   answerof-458702 ' value='1773165'   \/><label for='answer-id-1773165' id='answer-label-1773165' class=' answer'><span>Configure Amazon Bedrock guardrails with content filters set to high to protect against prompt injection attacks. Use a guardrail profile to implement cross-Region guardrail inference. Use Amazon CloudWatch Logs with custom metrics to capture detailed guardrail intervention events.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458702[]' id='answer-id-1773166' class='answer   answerof-458702 ' value='1773166'   \/><label for='answer-id-1773166' id='answer-label-1773166' class=' answer'><span>Configure Amazon Bedrock guardrails with content filters set to high. Use AWS WAF to block suspicious inputs. Use AWS CloudTrail to log API calls.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458702[]' id='answer-id-1773167' class='answer   answerof-458702 ' value='1773167'   \/><label for='answer-id-1773167' id='answer-label-1773167' class=' answer'><span>Deploy Amazon Comprehend custom classifiers to detect prompt injection attacks. Use Amazon API Gateway request validation. Use CloudWatch Logs to capture intervention events.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458702[]' id='answer-id-1773168' class='answer   answerof-458702 ' value='1773168'   \/><label for='answer-id-1773168' id='answer-label-1773168' class=' answer'><span>Configure Amazon Bedrock guardrails with custom content filters and word filters set to high. Configure cross-Region guardrail replication for failover. Store logs in AWS CloudTrail for compliance auditing.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-18' style=';'><div id='questionWrap-18'  class='   watupro-question-id-458703'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>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. <br \/>\r<br>Which solution will meet these requirements with the LEAST custom development effort?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='458703' \/><input type='hidden' id='answerType458703' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458703[]' id='answer-id-1773169' class='answer   answerof-458703 ' value='1773169'   \/><label for='answer-id-1773169' id='answer-label-1773169' class=' answer'><span>Configure 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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458703[]' id='answer-id-1773170' class='answer   answerof-458703 ' value='1773170'   \/><label for='answer-id-1773170' id='answer-label-1773170' class=' answer'><span>Create 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 to monitor demographic fairness. Set up Amazon CloudWatch alarms on the Guardrail Content Source dimension by using Invocations Intervened metrics to detect recommendation discrepancy threshold violations.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458703[]' id='answer-id-1773171' class='answer   answerof-458703 ' value='1773171'   \/><label for='answer-id-1773171' id='answer-label-1773171' class=' answer'><span>Set 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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458703[]' id='answer-id-1773172' class='answer   answerof-458703 ' value='1773172'   \/><label for='answer-id-1773172' id='answer-label-1773172' class=' answer'><span>Create 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 Invocations Intervened metrics with a dimension for each demographic group.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-19' style=';'><div id='questionWrap-19'  class='   watupro-question-id-458704'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>A company is developing a customer support application that uses Amazon Bedrock foundation models (FMs) to provide real-time AI assistance to the company\u2019s 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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='458704' \/><input type='hidden' id='answerType458704' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458704[]' id='answer-id-1773173' class='answer   answerof-458704 ' value='1773173'   \/><label for='answer-id-1773173' id='answer-label-1773173' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458704[]' id='answer-id-1773174' class='answer   answerof-458704 ' value='1773174'   \/><label for='answer-id-1773174' id='answer-label-1773174' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458704[]' id='answer-id-1773175' class='answer   answerof-458704 ' value='1773175'   \/><label for='answer-id-1773175' id='answer-label-1773175' class=' answer'><span>Implement direct frontend client connections to Amazon Bedrock by using IAM user credentials and the InvokeModelWithResponseStream API without any intermediate gateway or proxy layer.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458704[]' id='answer-id-1773176' class='answer   answerof-458704 ' value='1773176'   \/><label for='answer-id-1773176' id='answer-label-1773176' class=' answer'><span>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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-20' style=';'><div id='questionWrap-20'  class='   watupro-question-id-458705'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>A company is developing a generative AI (GenAI) application by using Amazon Bedrock. The application will analyze patterns and relationships in the company\u2019s data. The application will process millions of new data points daily across AWS Regions in Europe, North America, and Asia before storing the data in Amazon S3. <br \/>\r<br>The application must comply with local data protection and storage regulations. Data residency and processing must occur within the same continent. The application must also maintain audit trails of the application\u2019s decision-making processes and provide data classification capabilities. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_20' value='458705' \/><input type='hidden' id='answerType458705' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458705[]' id='answer-id-1773177' class='answer   answerof-458705 ' value='1773177'   \/><label for='answer-id-1773177' id='answer-label-1773177' class=' answer'><span>Deploy the application in each Region with local IAM policies. Use Amazon Bedrock cross-Region inference to distribute the workload. Use Amazon CloudWatch to log AI decision-making processes. Manually track compliance certifications across Regions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458705[]' id='answer-id-1773178' class='answer   answerof-458705 ' value='1773178'   \/><label for='answer-id-1773178' id='answer-label-1773178' class=' answer'><span>Use SCPs with AWS Organizations to manage location-specific permissions. Use AWS CloudTrail immutable logs to audit decision-making processes. Import a custom model into Amazon Bedrock and deploy the model to each Region.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458705[]' id='answer-id-1773179' class='answer   answerof-458705 ' value='1773179'   \/><label for='answer-id-1773179' id='answer-label-1773179' class=' answer'><span>Use Amazon S3 Object Lock with Region-specific S3 bucket policies. Pre-process the data points within the Region based on geographic origin before sending the data points to Amazon Bedrock. Use Amazon Macie to classify the data. Use AWS CloudTrail immutable logs to audit the decision-making processes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458705[]' id='answer-id-1773180' class='answer   answerof-458705 ' value='1773180'   \/><label for='answer-id-1773180' id='answer-label-1773180' class=' answer'><span>Create separate AWS accounts for each Region with individual compliance frameworks. Use Amazon SageMaker AI with custom monitoring. Create manual compliance reports for each regulatory jurisdiction.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-21' style=';'><div id='questionWrap-21'  class='   watupro-question-id-458706'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>A specialty coffee company has a mobile app that generates personalized coffee roast profiles by using Amazon Bedrock with a three-stage prompt chain. The prompt chain converts user inputs into structured metadata, retrieves relevant logs for coffee roasts, and generates a personalized roast recommendation for each customer. <br \/>\r<br>Users in multiple AWS Regions report inconsistent roast recommendations for identical inputs, slow inference during the retrieval step, and unsafe recommendations such as brewing at excessively high <br \/>\r<br>temperatures. The company must improve the stability of outputs for repeated inputs. The company must also improve app performance and the safety of the app\u2019s outputs. The updated solution must ensure 99.5% output consistency for identical inputs and achieve inference latency of less than 1 second. The solution must also block unsafe or hallucinated recommendations by using validated safety controls. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_21' value='458706' \/><input type='hidden' id='answerType458706' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458706[]' id='answer-id-1773181' class='answer   answerof-458706 ' value='1773181'   \/><label for='answer-id-1773181' id='answer-label-1773181' class=' answer'><span>Deploy Amazon Bedrock with provisioned throughput to stabilize inference latency. Apply Amazon Bedrock guardrails with semantic denial rules to block unsafe outputs. Use Amazon Bedrock Prompt Management to manage prompts by using approval workflows.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458706[]' id='answer-id-1773182' class='answer   answerof-458706 ' value='1773182'   \/><label for='answer-id-1773182' id='answer-label-1773182' class=' answer'><span>Use Amazon Bedrock Agents to manage chaining. Log model inputs and outputs to Amazon CloudWatch Logs. Use logs from CloudWatch to perform A\/B testing for prompt versions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458706[]' id='answer-id-1773183' class='answer   answerof-458706 ' value='1773183'   \/><label for='answer-id-1773183' id='answer-label-1773183' class=' answer'><span>Cache prompt results in Amazon ElastiCache. Use AWS Lambda functions to pre-process metadata and to trace end-to-end latency. Use AWS X-Ray to identify and remediate performance bottlenecks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458706[]' id='answer-id-1773184' class='answer   answerof-458706 ' value='1773184'   \/><label for='answer-id-1773184' id='answer-label-1773184' class=' answer'><span>Use Amazon Kendra to improve roast log retrieval accuracy. Store normalized prompt metadata within Amazon DynamoD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458706[]' id='answer-id-1773185' class='answer   answerof-458706 ' value='1773185'   \/><label for='answer-id-1773185' id='answer-label-1773185' class=' answer'><span>Use AWS Step Functions to orchestrate multi-step prompts.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-22' style=';'><div id='questionWrap-22'  class='   watupro-question-id-458707'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>A company uses Amazon Bedrock to implement a Retrieval Augmented Generation (RAG)-based system to serve medical information to users. The company needs to compare multiple chunking strategies, evaluate the generation quality of two foundation models (FMs), and enforce quality thresholds for deployment. <br \/>\r<br>Which Amazon Bedrock evaluation configuration will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_22' value='458707' \/><input type='hidden' id='answerType458707' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458707[]' id='answer-id-1773186' class='answer   answerof-458707 ' value='1773186'   \/><label for='answer-id-1773186' id='answer-label-1773186' class=' answer'><span>Create a retrieve-only evaluation job that uses a supported version of Anthropic Claude Sonnet as the evaluator model. Configure metrics for context relevance and context coverage. Define deployment thresholds in a separate CI\/CD pipeline.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458707[]' id='answer-id-1773187' class='answer   answerof-458707 ' value='1773187'   \/><label for='answer-id-1773187' id='answer-label-1773187' class=' answer'><span>Create a retrieve-and-generate evaluation job that uses custom precision-at-k metrics and an LLM-as-a-judge metric with a scale of 1C5. Include each chunking strategy in the evaluation dataset. Use a supported version of Anthropic Claude Sonnet to evaluate responses from both FMs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458707[]' id='answer-id-1773188' class='answer   answerof-458707 ' value='1773188'   \/><label for='answer-id-1773188' id='answer-label-1773188' class=' answer'><span>Create a separate evaluation job for each chunking strategy and FM combination. Use Amazon Bedrock built-in metrics for correctness and completeness. Manually review scores before deployment approval.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458707[]' id='answer-id-1773189' class='answer   answerof-458707 ' value='1773189'   \/><label for='answer-id-1773189' id='answer-label-1773189' class=' answer'><span>Set up a pipeline that uses multiple retrieve-only evaluation jobs to assess retrieval quality. Create separate evaluation jobs for both FMs that use Amazon Nova Pro as the LLM-as-a-judge model. Evaluate based on faithfulness and citation precision metrics.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-23' style=';'><div id='questionWrap-23'  class='   watupro-question-id-458708'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>A financial services company is developing a real-time generative AI (GenAI) assistant to support human call center agents. The GenAI assistant must transcribe live customer speech, analyze context, and provide incremental suggestions to call center agents while a customer is still speaking. To preserve responsiveness, the GenAI assistant must maintain end-to-end latency under 1 second from speech to initial response display. The architecture must use only managed AWS services and must support bidirectional streaming to ensure that call center agents receive updates in real time. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_23' value='458708' \/><input type='hidden' id='answerType458708' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773190' class='answer   answerof-458708 ' value='1773190'   \/><label for='answer-id-1773190' id='answer-label-1773190' class=' answer'><span>Use Amazon Transcribe streaming to transcribe calls. Pass the text to Amazon Comprehend for sentiment analysis. Feed the results to Anthropic Claude on Amazon Bedrock by using the Invoke Model AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773191' class='answer   answerof-458708 ' value='1773191'   \/><label for='answer-id-1773191' id='answer-label-1773191' class=' answer'><span>Store results in Amazon DynamoD<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773192' class='answer   answerof-458708 ' value='1773192'   \/><label for='answer-id-1773192' id='answer-label-1773192' class=' answer'><span>Use a WebSocket API to display the results.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773193' class='answer   answerof-458708 ' value='1773193'   \/><label for='answer-id-1773193' id='answer-label-1773193' class=' answer'><span>Use Amazon Transcribe streaming with partial results enabled to deliver fragments of transcribed text before customers finish speaking. Forward text fragments to Amazon Bedrock by using the InvokeModelWithResponseStream AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773194' class='answer   answerof-458708 ' value='1773194'   \/><label for='answer-id-1773194' id='answer-label-1773194' class=' answer'><span>Stream responses to call center agents through an Amazon API Gateway WebSocket AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773195' class='answer   answerof-458708 ' value='1773195'   \/><label for='answer-id-1773195' id='answer-label-1773195' class=' answer'><span>Use Amazon Transcribe batch processing to convert calls to text. Pass complete transcripts to Anthropic Claude on Amazon Bedrock by using the Converse Stream AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773196' class='answer   answerof-458708 ' value='1773196'   \/><label for='answer-id-1773196' id='answer-label-1773196' class=' answer'><span>Return responses through an Amazon Lex chatbot interface.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773197' class='answer   answerof-458708 ' value='1773197'   \/><label for='answer-id-1773197' id='answer-label-1773197' class=' answer'><span>Use the Amazon Transcribe streaming API with an AWS Lambda function to transcribe each audio segment. Call the Amazon Titan Embeddings model on Amazon Bedrock by using the InvokeModel AP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458708[]' id='answer-id-1773198' class='answer   answerof-458708 ' value='1773198'   \/><label for='answer-id-1773198' id='answer-label-1773198' class=' answer'><span>Publish results to Amazon SN<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-24' style=';'><div id='questionWrap-24'  class='   watupro-question-id-458709'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>A company is building a serverless application that uses AWS Lambda functions to help students around the world summarize notes. The application uses Anthropic Claude through Amazon Bedrock. The company observes that most of the traffic occurs during evenings in each time zone. Users report experiencing throttling errors during peak usage times in their time zones. <br \/>\r<br>The company needs to resolve the throttling issues by ensuring continuous operation of the application. The solution must maintain application performance quality and must not require a fixed hourly cost during low traffic periods. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='458709' \/><input type='hidden' id='answerType458709' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458709[]' id='answer-id-1773199' class='answer   answerof-458709 ' value='1773199'   \/><label for='answer-id-1773199' id='answer-label-1773199' class=' answer'><span>Create custom Amazon CloudWatch metrics to monitor model errors. Set provisioned throughput to a value that is safely higher than the peak traffic observed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458709[]' id='answer-id-1773200' class='answer   answerof-458709 ' value='1773200'   \/><label for='answer-id-1773200' id='answer-label-1773200' class=' answer'><span>Create custom Amazon CloudWatch metrics to monitor model errors. Set up a failover mechanism to redirect invocations to a backup AWS Region when the errors exceed a specified threshold.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458709[]' id='answer-id-1773201' class='answer   answerof-458709 ' value='1773201'   \/><label for='answer-id-1773201' id='answer-label-1773201' class=' answer'><span>Enable invocation logging in Amazon Bedrock. Monitor key metrics such as Invocations, InputTokenCount, OutputTokenCount, and InvocationThrottles. Distribute traffic across cross-Region inference endpoints.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458709[]' id='answer-id-1773202' class='answer   answerof-458709 ' value='1773202'   \/><label for='answer-id-1773202' id='answer-label-1773202' class=' answer'><span>Enable invocation logging in Amazon Bedrock. Monitor InvocationLatency, InvocationClientErrors, and InvocationServerErrors metrics. Distribute traffic across multiple versions of the same model.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-25' style=';'><div id='questionWrap-25'  class='   watupro-question-id-458710'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>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. <br \/>\r<br>The company must validate data quality, create auditable metadata, monitor data metrics, and <br \/>\r<br>customize text chunking to optimize foundation model (FM) performance. <br \/>\r<br>Which solution will meet these requirements with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='458710' \/><input type='hidden' id='answerType458710' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458710[]' id='answer-id-1773203' class='answer   answerof-458710 ' value='1773203'   \/><label for='answer-id-1773203' id='answer-label-1773203' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458710[]' id='answer-id-1773204' class='answer   answerof-458710 ' value='1773204'   \/><label for='answer-id-1773204' id='answer-label-1773204' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458710[]' id='answer-id-1773205' class='answer   answerof-458710 ' value='1773205'   \/><label for='answer-id-1773205' id='answer-label-1773205' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458710[]' id='answer-id-1773206' class='answer   answerof-458710 ' value='1773206'   \/><label for='answer-id-1773206' id='answer-label-1773206' class=' answer'><span>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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-26' style=';'><div id='questionWrap-26'  class='   watupro-question-id-458711'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>1.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. <br \/>\r<br>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. <br \/>\r<br>Which solution will meet these requirements with the LEAST development effort?<\/div><input type='hidden' name='question_id[]' id='qID_26' value='458711' \/><input type='hidden' id='answerType458711' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458711[]' id='answer-id-1773207' class='answer   answerof-458711 ' value='1773207'   \/><label for='answer-id-1773207' id='answer-label-1773207' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458711[]' id='answer-id-1773208' class='answer   answerof-458711 ' value='1773208'   \/><label for='answer-id-1773208' id='answer-label-1773208' class=' answer'><span>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 F<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458711[]' id='answer-id-1773209' class='answer   answerof-458711 ' value='1773209'   \/><label for='answer-id-1773209' id='answer-label-1773209' class=' answer'><span>Perform k-nearest neighbors (k-NN) searches to find semantically similar results.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458711[]' id='answer-id-1773210' class='answer   answerof-458711 ' value='1773210'   \/><label for='answer-id-1773210' id='answer-label-1773210' class=' answer'><span>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 PostgreSQ<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458711[]' id='answer-id-1773211' class='answer   answerof-458711 ' value='1773211'   \/><label for='answer-id-1773211' id='answer-label-1773211' class=' answer'><span>Create an AWS Lambda function to convert natural language queries to vector representations by using the same F<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458711[]' id='answer-id-1773212' class='answer   answerof-458711 ' value='1773212'   \/><label for='answer-id-1773212' id='answer-label-1773212' class=' answer'><span>Configure the Lambda function to perform similarity searches within the database.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458711[]' id='answer-id-1773213' class='answer   answerof-458711 ' value='1773213'   \/><label for='answer-id-1773213' id='answer-label-1773213' class=' answer'><span>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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-27' style=';'><div id='questionWrap-27'  class='   watupro-question-id-458712'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>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. <br \/>\r<br>The company confirms that most customer interactions are unique and that the solution recommends products not present in the product catalog. <br \/>\r<br>Which solution will meet this requirement?<\/div><input type='hidden' name='question_id[]' id='qID_27' value='458712' \/><input type='hidden' id='answerType458712' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458712[]' id='answer-id-1773214' class='answer   answerof-458712 ' value='1773214'   \/><label for='answer-id-1773214' id='answer-label-1773214' class=' answer'><span>Increase grounding within Amazon Bedrock Guardrails. Enable automated reasoning checks. Set up provisioned throughput.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458712[]' id='answer-id-1773215' class='answer   answerof-458712 ' value='1773215'   \/><label for='answer-id-1773215' id='answer-label-1773215' class=' answer'><span>Use prompt engineering to restrict model responses to relevant products. Use streaming inference to reduce perceived latency.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458712[]' id='answer-id-1773216' class='answer   answerof-458712 ' value='1773216'   \/><label for='answer-id-1773216' id='answer-label-1773216' class=' answer'><span>Create an Amazon Bedrock Knowledge Bases and implement Retrieval Augmented Generation (RAG). Set the PerformanceConfigLatency parameter to optimized.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458712[]' id='answer-id-1773217' class='answer   answerof-458712 ' value='1773217'   \/><label for='answer-id-1773217' id='answer-label-1773217' class=' answer'><span>Store product catalog data in Amazon OpenSearch Service. Validate model recommendations against the catalog. Use Amazon DynamoDB for response caching.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-28' style=';'><div id='questionWrap-28'  class='   watupro-question-id-458713'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>A financial services company is developing a Retrieval Augmented Generation (RAG) application to help investment analysts query complex financial relationships across multiple investment vehicles, market sectors, and regulatory environments. The dataset contains highly interconnected entities that have multi-hop relationships. Analysts must examine relationships holistically to provide accurate investment guidance. The application must deliver comprehensive answers that capture indirect relationships between financial entities and must respond in less than 3 seconds. <br \/>\r<br>Which solution will meet these requirements with the LEAST operational overhead?<\/div><input type='hidden' name='question_id[]' id='qID_28' value='458713' \/><input type='hidden' id='answerType458713' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458713[]' id='answer-id-1773218' class='answer   answerof-458713 ' value='1773218'   \/><label for='answer-id-1773218' id='answer-label-1773218' class=' answer'><span>Use Amazon Bedrock Knowledge Bases with GraphRAG and Amazon Neptune Analytics to store financial data. Analyze multi-hop relationships between entities and automatically identify related information across documents.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458713[]' id='answer-id-1773219' class='answer   answerof-458713 ' value='1773219'   \/><label for='answer-id-1773219' id='answer-label-1773219' class=' answer'><span>Use Amazon Bedrock Knowledge Bases and an Amazon OpenSearch Service vector store to implement custom relationship identification logic that uses AWS Lambda to query multiple vector embeddings in sequence.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458713[]' id='answer-id-1773220' class='answer   answerof-458713 ' value='1773220'   \/><label for='answer-id-1773220' id='answer-label-1773220' class=' answer'><span>Use Amazon OpenSearch Serverless vector search with k-nearest neighbor (k-NN). Implement manual relationship mapping in an application layer that runs on Amazon EC2 Auto Scaling.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458713[]' id='answer-id-1773221' class='answer   answerof-458713 ' value='1773221'   \/><label for='answer-id-1773221' id='answer-label-1773221' class=' answer'><span>Use Amazon DynamoDB to store financial data in a custom indexing system. Use AWS Lambda to query relevant records. Use Amazon SageMaker to generate responses.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-29' style=';'><div id='questionWrap-29'  class='   watupro-question-id-458714'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>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. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_29' value='458714' \/><input type='hidden' id='answerType458714' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458714[]' id='answer-id-1773222' class='answer   answerof-458714 ' value='1773222'   \/><label for='answer-id-1773222' id='answer-label-1773222' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458714[]' id='answer-id-1773223' class='answer   answerof-458714 ' value='1773223'   \/><label for='answer-id-1773223' id='answer-label-1773223' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458714[]' id='answer-id-1773224' class='answer   answerof-458714 ' value='1773224'   \/><label for='answer-id-1773224' id='answer-label-1773224' class=' answer'><span>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.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458714[]' id='answer-id-1773225' class='answer   answerof-458714 ' value='1773225'   \/><label for='answer-id-1773225' id='answer-label-1773225' class=' answer'><span>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.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-30' style=';'><div id='questionWrap-30'  class='   watupro-question-id-458715'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>A company upgraded its Amazon BedrockCpowered foundation model (FM) that supports a multilingual customer service assistant. After the upgrade, the assistant exhibited inconsistent behavior across languages. The assistant began generating different responses in some languages when presented with identical questions. <br \/>\r<br>The company needs a solution to detect and address similar problems for future updates. The evaluation must be completed within 45 minutes for all supported languages. The evaluation must process at least 15,000 test conversations in parallel. The evaluation process must be fully automated <br \/>\r<br>and integrated into the CI\/CD pipeline. The solution must block deployment if quality thresholds are not met. <br \/>\r<br>Which solution will meet these requirements?<\/div><input type='hidden' name='question_id[]' id='qID_30' value='458715' \/><input type='hidden' id='answerType458715' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458715[]' id='answer-id-1773226' class='answer   answerof-458715 ' value='1773226'   \/><label for='answer-id-1773226' id='answer-label-1773226' class=' answer'><span>Create a distributed traffic simulation framework that sends translation-heavy workloads to the assistant in multiple languages simultaneously. Use Amazon CloudWatch metrics to monitor latency, concurrency, and throughput. Run simulations before production releases to identify infrastructure bottlenecks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458715[]' id='answer-id-1773227' class='answer   answerof-458715 ' value='1773227'   \/><label for='answer-id-1773227' id='answer-label-1773227' class=' answer'><span>Deploy the assistant in multiple AWS Regions with Amazon Route 53 latency-based routing and AWS Global Accelerator to improve global performance. Store multilingual conversation logs in Amazon S3. Perform weekly post-deployment audits to review consistency.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458715[]' id='answer-id-1773228' class='answer   answerof-458715 ' value='1773228'   \/><label for='answer-id-1773228' id='answer-label-1773228' class=' answer'><span>Create a pre-processing pipeline that normalizes all incoming messages into a consistent format before sending the messages to the assistant. Apply rule-based checks to flag potential hallucinations in the outputs. Focus evaluation on normalized text to simplify testing across languages.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-458715[]' id='answer-id-1773229' class='answer   answerof-458715 ' value='1773229'   \/><label for='answer-id-1773229' id='answer-label-1773229' class=' answer'><span>Set up standardized multilingual test conversations with identical meaning. Run the test conversations in parallel by using Amazon Bedrock model evaluation jobs. Apply similarity and hallucination thresholds. Integrate the process into the CI\/CD pipeline to block releases that fail.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div style='display:none' id='question-31'>\n\t<div class='question-content'>\n\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/img\/loading.gif\" width=\"16\" height=\"16\" alt=\"Loading...\" title=\"Loading...\" \/>&nbsp;Loading...\t<\/div>\n<\/div>\n\n<br \/>\n\t\n\t\t\t<div class=\"watupro_buttons flex \" id=\"watuPROButtons11691\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; Previous\" onclick=\"WatuPRO.nextQuestion(event, 'previous');\"\/><\/div>\t\t  \t\t  \t\t   \n\t\t   \t  \t\t<div><input type=\"button\" name=\"action\" class=\"watupro-submit-button\" onclick=\"WatuPRO.submitResult(event)\" id=\"action-button\" value=\"View Results\"  \/>\n\t\t<\/div>\n\t\t<\/div>\n\t\t\n\t<input type=\"hidden\" name=\"quiz_id\" value=\"11691\" id=\"watuPROExamID\"\/>\n\t<input type=\"hidden\" name=\"start_time\" id=\"startTime\" value=\"2026-05-20 03:15:19\" \/>\n\t<input type=\"hidden\" name=\"start_timestamp\" id=\"startTimeStamp\" value=\"1779246919\" \/>\n\t<input type=\"hidden\" name=\"question_ids\" value=\"\" \/>\n\t<input type=\"hidden\" name=\"watupro_questions\" value=\"458686:1773095,1773096,1773097,1773098 | 458687:1773099,1773100,1773101,1773102,1773103 | 458688:1773104,1773105,1773106,1773107 | 458689:1773108,1773109,1773110,1773111 | 458690:1773112,1773113,1773114,1773115 | 458691:1773116,1773117,1773118,1773119,1773120 | 458692:1773121,1773122,1773123,1773124,1773125 | 458693:1773126,1773127,1773128,1773129 | 458694:1773130,1773131,1773132,1773133,1773134 | 458695:1773135,1773136,1773137,1773138 | 458696:1773139,1773140,1773141,1773142 | 458697:1773143,1773144,1773145,1773146 | 458698:1773147,1773148,1773149,1773150 | 458699:1773151,1773152,1773153,1773154,1773155 | 458700:1773156,1773157,1773158,1773159 | 458701:1773160,1773161,1773162,1773163,1773164 | 458702:1773165,1773166,1773167,1773168 | 458703:1773169,1773170,1773171,1773172 | 458704:1773173,1773174,1773175,1773176 | 458705:1773177,1773178,1773179,1773180 | 458706:1773181,1773182,1773183,1773184,1773185 | 458707:1773186,1773187,1773188,1773189 | 458708:1773190,1773191,1773192,1773193,1773194,1773195,1773196,1773197,1773198 | 458709:1773199,1773200,1773201,1773202 | 458710:1773203,1773204,1773205,1773206 | 458711:1773207,1773208,1773209,1773210,1773211,1773212,1773213 | 458712:1773214,1773215,1773216,1773217 | 458713:1773218,1773219,1773220,1773221 | 458714:1773222,1773223,1773224,1773225 | 458715:1773226,1773227,1773228,1773229\" \/>\n\t<input type=\"hidden\" name=\"no_ajax\" value=\"0\">\t\t\t<\/form>\n\t<p>&nbsp;<\/p>\n<\/div>\n\n<script type=\"text\/javascript\">\n\/\/jQuery(document).ready(function(){\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \t\nvar question_ids = \"458686,458687,458688,458689,458690,458691,458692,458693,458694,458695,458696,458697,458698,458699,458700,458701,458702,458703,458704,458705,458706,458707,458708,458709,458710,458711,458712,458713,458714,458715\";\nWatuPROSettings[11691] = {};\nWatuPRO.qArr = question_ids.split(',');\nWatuPRO.exam_id = 11691;\t    \nWatuPRO.post_id = 120796;\nWatuPRO.store_progress = 0;\nWatuPRO.curCatPage = 1;\nWatuPRO.requiredIDs=\"0\".split(\",\");\nWatuPRO.hAppID = \"0.40681400 1779246919\";\nvar url = \"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/show_exam.php\";\nWatuPRO.examMode = 1;\nWatuPRO.siteURL=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-admin\/admin-ajax.php\";\nWatuPRO.emailIsNotRequired = 0;\nWatuPROIntel.init(11691);\nWatuPRO.inCategoryPages=1;});    \t \n<\/script>\n","protected":false},"excerpt":{"rendered":"<p>The AWS Certified Generative AI Developer &#8211; Professional (AIP-C01) is a professional-level AWS certification focused on building and putting real-world generative AI applications into production (not just prototypes), with emphasis on effective use of foundation models via AWS tools like Amazon Bedrock, SageMaker, and related services. 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