AWS Certified Solutions Architect Professional SAP-C02 Practice Question
A global e-commerce company is redesigning its product-catalog service. The new architecture must:
Sustain up to 500 000 writes per second during vendor bulk uploads.
Return catalog items with single-digit-millisecond latency to web and mobile clients.
Let analysts run complex ad-hoc SQL queries across the entire catalog once per hour without affecting the transactional workload.
Minimize operational overhead and ongoing cost.
Which approach will meet these requirements MOST effectively?
Store the catalog in sharded Amazon DocumentDB clusters. Use AWS Glue jobs to copy the data hourly to Amazon S3 and query it with Amazon Redshift Spectrum.
Store the catalog in Amazon DynamoDB using on-demand capacity. Enable point-in-time recovery and schedule hourly DynamoDB table exports to Amazon S3. Analysts query the exported data in S3 with Amazon Athena.
Deploy Amazon Aurora MySQL with provisioned writer and multiple read replicas. Take hourly automated snapshots, load them into Amazon Redshift with AWS DMS, and let analysts query the Redshift cluster.
Persist catalog documents in Amazon OpenSearch Service with UltraWarm storage. Use the OpenSearch SQL plugin for both transactional reads and analytic queries.
Amazon DynamoDB in on-demand mode delivers single-digit-millisecond latency and can scale to hundreds of thousands of writes per second without capacity planning. Enabling point-in-time recovery lets you export the table to Amazon S3 asynchronously and without consuming read capacity. Analysts can then use Amazon Athena to run serverless SQL directly against the exported objects in S3, isolating analytic scans from the transactional store. The alternative solutions either cannot handle the write throughput, couple analytic queries to the primary database, or require additional clusters and complex ETL processes that add cost and operational burden.
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AWS Certified Solutions Architect Professional SAP-C02
Design for New Solutions
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