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Workload-Specific Database DesignhardMultiple ChoiceObjective-mapped

DBS-C01 Workload-Specific Database Design Practice Question

A social media company uses Amazon DynamoDB to store user posts. The table has a partition key of 'user_id' and a sort key of 'post_timestamp'. Each item is about 10 KB. The application needs to retrieve all posts for a given user within a date range. The company recently added a new feature that allows users to 'like' posts, and they store the like count as an attribute in the post item. The like count is updated frequently. The application experiences high write throttling on the table. The table has 1000 WCUs provisioned. The write pattern is bursty. Which design change would MOST effectively reduce write throttling?

⚠ Common exam trap

Candidates often assume increasing provisioned capacity (Option A) is the universal fix for throttling, but AWS specifically tests the understanding that DynamoDB's partition-level throughput limits require data distribution changes, not just capacity increases.

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

Add a random suffix to the user_id partition key to distribute writes across multiple partitions.

The write throttling is caused by a 'hot partition' — all writes for a given user_id go to the same partition, and the bursty write pattern (e.g., many likes on a single post) exceeds the partition's 1,000 WCU limit (1,000 write capacity units per partition). Adding a random suffix to the user_id partition key distributes writes across multiple partitions, effectively increasing the write throughput for that logical user's data. This is a common design pattern for DynamoDB to handle high-traffic items without increasing provisioned capacity.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Increase the WCUs to 5000.

    Why it's wrong here

    Increasing capacity helps but is not the most efficient; sharding is more cost-effective.

  • Enable DynamoDB Accelerator (DAX) to cache writes.

    Why it's wrong here

    DAX is a read cache, it does not help write throttling.

  • Add a random suffix to the user_id partition key to distribute writes across multiple partitions.

    Why this is correct

    Sharding spreads the write load evenly across partitions, reducing throttling.

  • Create a Global Secondary Index (GSI) on the like count attribute.

    Why it's wrong here

    A GSI does not reduce write throttling on the base table.

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