{"id":444,"date":"2023-01-14T11:08:05","date_gmt":"2023-01-14T11:08:05","guid":{"rendered":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/aws-certified-machine-learning-specialty-mls-c01-question045\/"},"modified":"2023-01-14T11:08:05","modified_gmt":"2023-01-14T11:08:05","slug":"aws-certified-machine-learning-specialty-mls-c01-question045","status":"publish","type":"post","link":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/aws-certified-machine-learning-specialty-mls-c01-question045\/","title":{"rendered":"AWS Certified Machine Learning &#8211; Specialty MLS-C01 &#8211; Question045"},"content":{"rendered":"<div class=\"question\">A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.<br \/>\nThe ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.<br \/>\nWhich solution should the Data Scientist build to satisfy the requirements?<br \/><strong><br \/>A.<\/strong> Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.<br \/><strong>B.<\/strong> Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.<br \/><strong>C.<\/strong> Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.<br \/><strong>D.<\/strong> Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.<\/div>\n<p><\/p>\n<style> .hidden-div{ display:none } <\/style>\n<p>\t\t\t\t\t\t\t<button onclick=\"getElementById('hidden-div').style.display = 'block'\"> Show Answer <\/button> <button onclick=\"getElementById('hidden-div').style.display = 'none'\">Hide Answer<\/button><\/p>\n<div class=\"hidden-div\" id=\"hidden-div\"><span style=\"\"><\/p>\n<div class=\"answer\">Correct Answer: <strong>A<\/strong><\/div>\n<p><\/strong><\/span> <\/div>\n","protected":false},"excerpt":{"rendered":"<p>A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data. The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[3,48],"class_list":["post-444","post","type-post","status-publish","format-standard","hentry","category-aws-certified-machine-learning-specialty-mls-c01","tag-aws-certified-machine-learning-specialty-mls-c01","tag-question-045"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/posts\/444","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/comments?post=444"}],"version-history":[{"count":0,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/posts\/444\/revisions"}],"wp:attachment":[{"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/media?parent=444"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/categories?post=444"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/exampracticetests.com\/aws\/Machine_Learning-Specialty_MLS-C01\/wp-json\/wp\/v2\/tags?post=444"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}