AWS Certified Data Engineer Associate DEA-C01 Practice Question
A mobile gaming company ingests about 20,000 JSON session events per second from players worldwide. Data engineers must store the events so that the latest session for any player can be retrieved in less than 10 ms while writes remain single-digit milliseconds. Traffic varies widely during promotions, and the team wants to avoid manual capacity planning. Which storage solution best meets these performance and operational requirements?
Create a DynamoDB table with playerId as the partition key, use on-demand capacity mode, and add a two-node DAX cluster for caching.
Write the events to Amazon S3 in Parquet format and query the latest session with Amazon Athena using hourly partitions.
Store events in an Amazon RDS MySQL Multi-AZ instance configured with 20,000 provisioned IOPS and a read replica for scaling reads.
Stream the data directly into an Amazon Redshift cluster using the native streaming ingestion API and define playerId as the sort key.
DynamoDB is purpose-built for key-value workloads that need single-digit millisecond latency at any scale. Using the player ID as the partition key allows efficient lookups of the most recent session record. Enabling on-demand capacity mode lets the table automatically scale up or down with traffic, eliminating manual provisioning. Adding a DynamoDB Accelerator (DAX) cluster further reduces read latency to microseconds without affecting write performance.
Amazon RDS with provisioned IOPS can achieve low latency for some workloads but requires manual instance sizing and may not sustain 20,000 TPS while keeping sub-10 ms latency under variable load. Amazon Redshift is optimized for analytic, not millisecond, query patterns and its streaming ingestion is limited compared with DynamoDB's write scalability. Querying data in Amazon S3 with Athena is cost-effective for ad-hoc analytics but cannot deliver millisecond-level read or write latency.
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AWS Certified Data Engineer Associate DEA-C01
Data Store Management
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