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Workload-Specific Database DesignhardMultiple SelectObjective-mapped

DBS-C01 Workload-Specific Database Design Practice Question

Which THREE design patterns can improve the performance of a write-heavy application using Amazon DynamoDB?

⚠ Common exam trap

Test-takers frequently confuse local secondary indexes (LSIs) with global secondary indexes (GSIs) or assume that increasing WCUs alone resolves hot partitions, ignoring DynamoDB's per-partition throughput limits.

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

Write sharding by using a composite key with a random suffix to distribute writes across partitions.

Write sharding with a random suffix on the partition key distributes writes evenly across multiple partitions, preventing hot partitions. This pattern avoids throttling by ensuring no single partition exceeds its write capacity limit, which is critical for write-heavy workloads in DynamoDB.

Answer analysis

Option-by-option breakdown

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

  • Write sharding by using a composite key with a random suffix to distribute writes across partitions.

    Why this is correct

    Prevents hot partitions by evenly distributing write traffic.

  • Enable DynamoDB adaptive capacity to allow a single partition to use more throughput.

    Why this is correct

    Adaptive capacity helps handle uneven access patterns.

  • Create local secondary indexes (LSIs) for all query patterns.

    Why it's wrong here

    LSIs can increase write costs and do not improve write performance.

  • Use DynamoDB Accelerator (DAX) to offload read traffic.

    Why this is correct

    DAX reduces read load on the table, freeing capacity for writes.

  • Increase provisioned write capacity units (WCUs) to the maximum allowed.

    Why it's wrong here

    Simply increasing WCUs may not solve partition-level throttling.

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Last reviewed: Jun 24, 2026

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