AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company ingests 50,000 IoT sensor readings per second. Each record is less than 1 KB of JSON. Data must be available for dashboards that query individual device readings with single-digit millisecond latency. Records are retained for 30 days, after which they should be automatically removed without administrator intervention. Which AWS storage service best meets these requirements while minimizing operational overhead?
Amazon DynamoDB with TTL enabled on the ingestion timestamp
Amazon Aurora MySQL with read replica autoscaling
Amazon Redshift cluster using automatic table vacuum and retention policies
Amazon DynamoDB delivers consistent single-digit millisecond latency at any scale, making it suitable for real-time lookups of individual sensor readings. The service is fully managed and automatically scales throughput to handle sustained ingestion rates like 50,000 writes per second. DynamoDB Time to Live (TTL) can be enabled on the timestamp attribute so items expire automatically after 30 days, eliminating manual cleanup. Amazon S3 is cost-effective but cannot provide millisecond point-read latency for individual items. Amazon RDS Aurora can achieve sub-second queries but would require careful sharding and manual capacity management to sustain 50,000 TPS, increasing operational effort. Amazon Redshift is optimized for analytic scans, not high-velocity, per-item transactions, and it offers second-level rather than millisecond response times. Therefore, DynamoDB is the most appropriate choice.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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