DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You need to monitor the performance of your Azure Synapse Analytics dedicated SQL pool. Which metric should you use to identify queued queries due to concurrency limits?
⚠ Common exam trap
DP-203 often tests the distinction between metrics that indicate resource saturation (DWU percentage, memory percentage) and those that specifically indicate concurrency queuing, so candidates must know that 'Queued queries' is the direct signal.
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
✓
Queued queries
The 'Queued queries' metric in Azure Synapse Analytics dedicated SQL pool specifically counts queries waiting for resources due to concurrency limits, making it the direct indicator of queuing caused by insufficient concurrency slots. Monitoring this metric helps identify when the workload exceeds available concurrency and queries are being held in the queue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Queued queries
Why this is correct
The queued queries metric counts requests waiting for a concurrency slot in the dedicated SQL pool. Rising values indicate that active queries have exhausted available slots, directly identifying concurrency-limit queuing rather than resource pressure such as DWU saturation or tempdb usage.
- ✗
DWU percentage
Why it's wrong here
DWU percentage reports compute utilisation against provisioned Data Warehouse Units, not the number of requests waiting for a concurrency slot. It is tempting because it genuinely reflects resource pressure and scaling headroom, making it the right metric for deciding when to scale a dedicated SQL pool up or down.
- ✗
Active queries
Why it's wrong here
Active queries counts queries currently executing, not those waiting for a concurrency slot, so it cannot reveal queueing caused by concurrency limits. It is tempting because it does track query activity, and it would suit monitoring overall workload volume or execution counts rather than identifying blocked, queued requests.
- ✗
Memory percentage
Why it's wrong here
Memory percentage reflects how much of the pool's memory is consumed, which can be high while concurrency slots remain free. It suits diagnosing memory pressure or spill-to-tempdb issues, not identifying queries queued behind concurrency limits.
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JA
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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