DEA-C01 Data Store Management Practice Question
A data engineer maintains an Amazon DynamoDB table that stores device telemetry. The table uses a partition key of deviceId and a sort key of timestamp, with on-demand capacity mode. A new fleet of devices writes data with a deviceId pattern that hashes to a small number of partitions, and the engineer observes throttling on writes even though consumed capacity is well below any configured limit. Which change addresses the root cause?
⚠ Common exam trap
The trap here is treating throttling as a capacity shortage and raising throughput, when the real cause is a partition key whose hash space is too small.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Redesign the key schema to use a higher-cardinality partition key, such as a composite of deviceId and a shard suffix, while keeping timestamp as the sort key.
Throttling at low consumed capacity on a DynamoDB table almost always indicates a hot partition caused by an uneven partition key. Because data is distributed by the hash of the partition key, a key with few distinct values funnels traffic into a small number of partitions. Adding a shard suffix to the partition key spreads the writes while preserving sort-key range queries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a global secondary index on timestamp and direct all writes through that index to distribute the load.
Why it's wrong here
Writes always go to the base table first; a global secondary index is updated asynchronously from those writes and inherits the same partition key distribution problem. Adding an index on timestamp increases write cost and storage without relieving the hot partitions, so throttling on the base table continues.
- ✗
Switch the table from on-demand to provisioned capacity mode and raise the write capacity units substantially.
Why it's wrong here
On-demand mode already scales automatically with traffic, so throttling at low consumed capacity is not a capacity-unit shortage. Provisioned mode would not remove the hot-partition effect and would add the burden of forecasting throughput, leaving the uneven key distribution as the actual cause of the throttling.
- ✓
Redesign the key schema to use a higher-cardinality partition key, such as a composite of deviceId and a shard suffix, while keeping timestamp as the sort key.
Why this is correct
DynamoDB partitions data by the hash of the partition key, so a low-cardinality key concentrates writes onto a few partitions and triggers throttling regardless of overall table capacity. Introducing a shard suffix spreads writes across many partitions while the sort key still allows efficient range queries per device, directly removing the hot-partition bottleneck.
- ✗
Enable DynamoDB Streams on the table so that write operations are buffered and replayed during peak periods.
Why it's wrong here
DynamoDB Streams captures an ordered log of item changes for downstream processing, but it does not buffer or defer the original writes and cannot prevent throttling. The stream only records changes after they succeed, so enabling it leaves the uneven partition key distribution untouched.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.