AWS Certified Data Engineer Associate DEA-C01 Practice Question
A gaming company captures real-time session events from Amazon Kinesis Data Streams. The backend must persist each player's most recent 24-hour session data, handle unpredictable spikes to millions of writes per second, and return player records in single-digit milliseconds by primary key. Operations wants a fully managed, auto-scaling or serverless solution with built-in TTL so stale data is deleted automatically. Which AWS data store best meets these requirements?
Amazon S3 bucket storing JSON objects queried through Amazon Athena and S3 Lifecycle rules
Amazon Redshift streaming ingestion into an RA3 cluster with automatic table sort keys
Amazon Aurora MySQL Serverless v2 cluster with auto-scaling read/write endpoints
Amazon DynamoDB table with on-demand capacity and TTL enabled
Amazon DynamoDB offers on-demand capacity or DynamoDB Serverless, which automatically scales to millions of requests per second without capacity planning. It consistently delivers single-digit millisecond latency for key-value access and supports a TTL attribute that automatically removes items after a specified timestamp, satisfying the 24-hour retention goal. Aurora MySQL Serverless cannot sustain millisecond retrieval under extreme write bursts and introduces connection and cold-start latency. Amazon S3 with Athena provides virtually unlimited storage but has query latencies in seconds and no native per-item TTL. Amazon Redshift is optimized for analytic, not per-row, millisecond queries and requires manual cleanup or VACUUM operations. Therefore, DynamoDB is the most suitable choice.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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