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Scenarios include transforming nested JSON clickstream data into tabular form for Amazon Redshift, building scalable Kinesis-based session timelines, storing per-case PDF files with minimal cost, and designing near-real-time fraud detection to minimize overall latency using SageMaker. Additional items address migrating Hadoop processing scripts to EMR while reusing Java code.",{"@graph":69,"@context":115},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/aws-certified-data-analytics-specialty-sample-questions-v11-final/317428/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/aws-certified-data-analytics-specialty-sample-questions-v11-final/317428.png","ImageObject",300,407,{"name":92,"@type":93},"Adam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-20",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109],{"name":110,"@type":111,"acceptedAnswer":112},"What design achieves the lowest overall latency for fraud prevention with SageMaker?","Question",{"text":113,"@type":114},"Use streaming ingestion that allows direct triggering/invocation of SageMaker with minimal buffering. Among the options, the solution based on Kinesis Data Streams with Lambda for invocation targets lower end-to-end latency than Firehose buffering or indirect messaging approaches.","Answer","https://schema.org",{"og:url":83,"og:type":117,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":119,"canonical":83},"index,follow",{"doc_id":121,"site_id":62},317428,1790027567,{"code":4,"msg":5,"data":124},{"doc_id":121,"user_id":125,"nickname":92,"user_avatar":126,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":127,"file_id":128,"file_url":129,"file_type":130,"file_size":131,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"language":132,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":133,"faqs":134,"seo_title":135,"seo_description":67,"update_tm":136,"read_time":137,"preview_page_count":4},1374404737137,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","1) A company ingests a large set of clickstream data in nested JSON format from different sources and stores it in Amazon S3 . Data Analysts need to analyze this data in combination with data stored in an Amazon Redshift cluster. Data Analysts want to build a cost-effective and automated solution for this need.  \nWhich solution meets these requirements?  \nA) Use Apache Spark SQL on Amazon EMR to convert the clickstream data to a tabular format. Use the Amazon Redshift COPY command to load the data into the Amazon Redshift cluster.  \nB) Use AWS Lambda to convert the data to a tabular format and write it to Amazon S3 . Use the Amazon Redshift COPY command to load the data into the Amazon Redshift cluster.  \nC) Use the Relationalize class in an AWS Glue ETL job to transform the data and write the data back to Amazon S3 . Use Amazon Redshift Spectrum to create external tables and join with the internal tables.  \nD) Use the Amazon Redshift COPY command to move the clickstream data directly into new tables in the Amazon Redshift cluster.  \n2) A Publisher website captures user activity and sends clickstream data to Amazon Kinesis Data Streams. The Publisher wants to design a cost-effective solution to process the data to create a timeline of user activity within a session. The solution must be able to scale depending on the number of active sessions.  \nWhich solution meets these requirements?  \nA) Include a variable in the clickstream data from the Publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on Amazon EC2 instances in an EC2 Auto Scaling group.  \nB) Include a variable in the clickstream to maintain a counter for each user action during their session. Use the action type as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on AWS Lambda.  \nC) Include a session identifier in the clickstream data from the Publisher website and use as the partition key for the stream. Use the Kinesis Client Library (KCL) in the consumer application to retrieve the data from the stream and perform the processing. Deploy the consumer application on Amazon EC2 instances in an EC2 Auto Scaling group. Use an AWS Lambda function to reshard the stream based upon Amazon CloudWatch alarms.  \nD) Include a variable in the clickstream data from the Publisher website to maintain a counter for the number of active user sessions. Use a timestamp for the partition key for the stream. Configure the consumer application to read the data from the stream and change the number of processor threads based upon the counter. Deploy the consumer application on AWS Lambda.  \n3) A company is currently using Amazon DynamoDB as the database for a user support application. The company is developing a new version of the application that will store a PDF file for each support case ranging in size from 1–10 MB. The file should be retrievable whenever the case is accessed in the application.  \nHow can the company store the file in the MOST cost-effective manner?  \nA) Store the file in Amazon DocumentDB and the document ID as an attribute in the DynamoDB table.  \nB) Store the file in Amazon S3 and the object key as an attribute in the DynamoDB table.  \nC) Split the file into smaller parts and store the parts as multiple items in a separate DynamoDB table.  \nD) Store the file as an attribute in the DynamoDB table using Base64 encoding.  \n4) A company needs to implement a near-real-time fraud prevention feature for its ecommerce site. User and order details need t","cbCaifaA4pECFDBX","https://ap.wps.com/l/cbCaifaA4pECFDBX","pdf",289361,"English","# Data ingestion and transformation\n## Clickstream to Amazon Redshift\n## Kinesis session timeline processing\n# Data storage strategy\n## Cost-effective file storage with DynamoDB and S3\n# Real-time fraud prevention\n## Lowest-latency inference flow to SageMaker\n# Migrating Hadoop to EMR\n## Reusing Java code in EMR steps","[{\"question\":\"What design achieves the lowest overall latency for fraud prevention with SageMaker?\",\"answer\":\"Use streaming ingestion that allows direct triggering/invocation of SageMaker with minimal buffering. Among the options, the solution based on Kinesis Data Streams with Lambda for invocation targets lower end-to-end latency than Firehose buffering or indirect messaging approaches.\"}]","AWS Certified Data Analytics Specialty - Sample Questions - v.1.1 Final | PDF",1789932729,13]