AWS Certified Data Engineer Associate DEA-C01 Practice Question
A logistics company streams 50,000 vehicle telemetry records per second into AWS. The application must read the latest records for a given vehicle ID in less than 5 ms, automatically purge data older than 7 days, and retain the expired data in Amazon S3 for long-term analytics. Which storage configuration best meets these access and retention requirements with the least operational overhead?
Insert the records into an Amazon RDS PostgreSQL database with a composite primary key; schedule a daily DELETE job for aged rows and export snapshots to S3.
Load the data into an Amazon Redshift cluster, run a nightly DELETE on rows older than 7 days, and UNLOAD the deleted data to S3 for retention.
Store the records in a DynamoDB table with vehicleId as the partition key and timestamp as the sort key, enable DynamoDB TTL for 7 days, and use DynamoDB export to Amazon S3 for archive.
Deliver the stream directly to Amazon S3 using Kinesis Data Firehose, query recent data with Amazon Athena, and apply an S3 Lifecycle rule to transition objects after 7 days.
A DynamoDB table that uses vehicleId as the partition key and timestamp as the sort key supports highly scalable writes and single-digit millisecond queries that satisfy the 5 ms latency target. Enabling DynamoDB TTL automatically marks items older than 7 days for deletion, eliminating the need for custom cleanup jobs. The native DynamoDB table export to Amazon S3 delivers the expired data to S3 without requiring code or ETL pipelines, meeting the archival requirement with minimal administration.
An RDS database can handle the workload only with extensive scaling and creates ongoing maintenance tasks for deletes and exports. Storing the data directly in S3 with Athena cannot achieve millisecond lookups for individual vehicle records. Using Amazon Redshift is optimized for analytical rather than per-item lookups and still needs custom delete and unload processes, increasing operational burden.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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