AWS Certified Data Engineer Associate DEA-C01 Practice Question
An e-commerce company ingests about 100 000 clickstream events per second. Data engineers must store each event with minimal latency and allow customer-support dashboards to fetch the most recent 30 days of events for a single user in milliseconds. The solution must require little operational overhead and automatically remove data older than 30 days. Which storage option best fits these access patterns and requirements?
Insert events into an Amazon RDS for PostgreSQL instance with multiple read replicas and schedule daily purges of old rows.
Stream events into an Amazon Redshift cluster with sort keys on user ID and event time for analytical queries.
Write events as Parquet files to Amazon S3 partitioned by date and query them with Amazon Athena on demand.
Store events in an Amazon DynamoDB table keyed by user ID and timestamp, enable on-demand capacity, and configure TTL for 30-day expiry.
Fetching events for an individual user requires key-value lookups with consistent single-digit millisecond latency while handling very high write throughput. A DynamoDB table that uses the user ID as the partition key and the event timestamp as the sort key scales horizontally without manual maintenance, provides millisecond reads and writes, and supports Time to Live (TTL) to delete items automatically after 30 days. Amazon S3 queried by Athena, Amazon Redshift, and Amazon RDS can all store the data, but each introduces seconds-level query times or intensive cluster management that do not meet the low-latency and minimal-operations requirements for this workload.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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