AWS Certified Data Engineer Associate DEA-C01 Practice Question
A company ingests IoT sensor data into a DynamoDB table. Baseline traffic is 2,000 writes/s, but spikes to 20,000 writes/s for 10 minutes several times daily. Provisioning 20,000 WCU constantly avoids throttling but is costly. They want a cheaper solution that prevents throttled writes during spikes and needs little operations. Which option meets these needs?
Create a scheduled scaling action that increases the table to 20,000 WCU every hour and reduces it afterward.
Convert the table to on-demand capacity mode.
Enable DynamoDB auto scaling with a minimum of 2,000 and a maximum of 20,000 WCU.
Shard the data across ten new DynamoDB tables chosen by a hash of device ID to distribute the write load.
Switching the table to on-demand capacity mode is the most economical and reliable choice. On-demand instantly accommodates traffic up to the previous peak (and can rapidly scale beyond it) without manual intervention, so the 20,000-WCU surges are served without throttling. Because charges are per request, the company pays for roughly 2,000 WCU most of the day and only pays the higher rate during short bursts, reducing overall cost.
Auto scaling in provisioned mode can lower cost but cannot react within seconds; scale-out actions usually take several minutes, so sudden surges may still be throttled. Scheduled scaling requires knowing exact spike times, which is impractical and leaves excess capacity outside those windows. Sharding the workload across multiple tables adds complexity and still aggregates to similar peak capacity, offering no meaningful cost advantage.
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AWS Certified Data Engineer Associate DEA-C01
Data Ingestion and Transformation
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