DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You manage an Azure Synapse Analytics dedicated SQL pool. A nightly ELT job loads a large fact table and then runs UPDATE statements on many rows. You observe that tempdb usage grows until the load fails. You need to reduce tempdb pressure during the update phase. What should you do?
⚠ Common exam trap
The trap here is believing that scaling up the service level or enabling a caching feature will fix tempdb pressure, when the real cause is the modification pattern itself.
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
✓
Replace the row-by-row updates with a CTAS-based pattern that rebuilds the affected partitions.
Large UPDATE operations in dedicated SQL pools incur significant logging and data movement that consume tempdb. The recommended approach for substantial changes is to use CREATE TABLE AS SELECT to produce a new version of the affected partitions and then switch them in. This avoids the row-level update engine path, reduces tempdb growth, and is faster for bulk modifications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable result set caching on the dedicated SQL pool.
Why it's wrong here
Result set caching stores the output of eligible SELECT queries to speed up repeated reads. It has no effect on data modification operations such as UPDATE, and it does not alter how those statements use tempdb. Enabling it would not reduce the tempdb growth observed during the update phase of the ELT job.
- ✗
Increase the size of tempdb by scaling the dedicated SQL pool to a higher DWU.
Why it's wrong here
Scaling to a higher service level allocates more compute and storage resources, but tempdb capacity in a dedicated SQL pool is tied to the service level and cannot be directly resized. More importantly, scaling does not change the update mechanism that produces the tempdb pressure, so the underlying problem would recur even with more resources.
- ✓
Replace the row-by-row updates with a CTAS-based pattern that rebuilds the affected partitions.
Why this is correct
Dedicated SQL pool UPDATE statements are implemented internally and can generate large amounts of movement and logging that spill into tempdb. Rebuilding affected partitions with CREATE TABLE AS SELECT writes new data directly and swaps partitions, avoiding the row-level update path. This is the documented pattern for large modifications and directly reduces tempdb consumption during the load window.
- ✗
Change the distribution of the fact table to ROUND_ROBIN.
Why it's wrong here
ROUND_ROBIN distribution spreads rows evenly but prevents effective partition elimination and can increase data movement for joins and updates. It does not eliminate the tempdb usage caused by large UPDATE statements; in fact, it may worsen performance for filtered modifications because rows for a given key are scattered across distributions.
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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 Microsoft exam blueprint
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