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Workload-Specific Database DesignmediumMultiple ChoiceObjective-mapped

DBS-C01 Workload-Specific Database Design Practice Question

A company has a document database workload on Amazon DynamoDB that stores user session data. The application frequently updates session attributes (e.g., last activity timestamp). The current design stores the entire session as a single item and updates the entire item on each session activity. This is causing high write costs and throttling. Which design pattern would reduce write costs and improve performance?

⚠ Common exam trap

Candidates often confuse scaling solutions (increasing WCUs or adding DAX) with optimization patterns, failing to recognize that the real issue is the write amplification caused by full-item updates rather than insufficient capacity or read performance.

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

Use UpdateItem with an update expression to modify only the changed attributes.

Using UpdateItem with an update expression allows you to modify only the specific attributes that changed (e.g., last activity timestamp) instead of rewriting the entire item. This reduces write consumption to a fraction of the original cost, since DynamoDB charges based on the size of the written data, and partial updates write only the changed attribute bytes. This directly addresses the high write costs and throttling caused by full-item overwrites.

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 write capacity units (WCUs) on the table.

    Why it's wrong here

    Increasing capacity increases cost and does not reduce the write unit consumption per update.

  • Use UpdateItem with an update expression to modify only the changed attributes.

    Why this is correct

    Update expressions only write the changed attributes, consuming fewer write capacity units.

  • Implement DynamoDB Accelerator (DAX) to cache the session data.

    Why it's wrong here

    DAX is a read cache and does not reduce write costs.

  • Split the session item into multiple items, one per attribute.

    Why it's wrong here

    Splitting items complicates queries and may increase overall write cost due to multiple item updates.

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