DBS-C01 Workload-Specific Database Design Practice Question

A company is using Amazon DynamoDB for a shopping cart application. The table has a partition key of `user_id` and a sort key of `item_id`. The application performs frequent updates to the `quantity` attribute. The company notices that write requests are being throttled during peak hours. Which TWO actions would help reduce throttling? (Choose two.)

⚠ Common exam trap

Many candidates confuse read-side solutions (like DAX or Streams) with write-side throttling, or they mistakenly think conditional writes reduce capacity consumption, when in fact they do not address the root cause of insufficient write capacity or hot partitions.

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

✓

Increase the provisioned write capacity for the table.

Increasing the provisioned write capacity directly raises the number of write capacity units (WCUs) available per second, allowing more write requests to succeed without being throttled. Since the application performs frequent updates to the `quantity` attribute, which consumes write capacity, adding more capacity alleviates throttling during peak hours.

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 provisioned write capacity for the table.
Why this is correct
Increasing write capacity directly reduces throttling.
✗
Use conditional writes to prevent overwrites.
Why it's wrong here
Conditional writes reduce unnecessary updates but do not address throttling from high write volume.
✓
Implement a write sharding pattern using a random suffix on the partition key.
Why this is correct
Implementing a write sharding pattern with a random suffix on the partition key distributes writes across multiple physical partitions, preventing a single hot partition from throttling the `quantity` updates for a given `user_id`. This directly addresses the stem’s constraint of frequent updates to a single attribute under a shared partition key during peak hours, as DynamoDB’s 1,000 write capacity units per partition limit is avoided by spreading the load.
✗
Enable DynamoDB Streams to process writes asynchronously.
Why it's wrong here
Streams capture changes but do not reduce write throttling.
✗
Enable DynamoDB Accelerator (DAX) for the table.
Why it's wrong here
DAX is a read cache and does not improve write throughput.

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DBS-C01 Workload-Specific Database Design Practice Question

Why each option matters

Option-by-option breakdown

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