DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are monitoring Azure Stream Analytics job performance. The job is falling behind in processing real-time data. You notice that the SU (Streaming Unit) utilization is consistently at 90% or higher. What is the most appropriate action to improve throughput?
⚠ Common exam trap
DP-203 often tests the misconception that optimizing query logic or output partitioning can solve performance issues when the root cause is insufficient compute resources, leading candidates to choose options other than scaling SUs.
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 number of Streaming Units (SUs)
Streaming Unit (SU) utilization consistently at 90% or higher indicates the job is resource-constrained. Increasing the number of SUs allocates more compute resources, which can improve throughput and reduce backlog. This is the most direct and appropriate action to scale the job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the output to use a partition scheme
Why it's wrong here
Partitioning the output improves parallel write throughput to the sink, but the bottleneck is SU utilisation at 90%, meaning compute is saturated before output; partitioning cannot add processing capacity. It is tempting when sink throttling or write contention limits throughput, which is the scenario where output partitioning genuinely helps.
- ✗
Reduce the window duration in the query
Why it's wrong here
Shortening the window duration changes query semantics and may increase the frequency of emitted results, but it does not reduce the compute load per event, so saturated Streaming Units remain the constraint. It is tempting when latency, not throughput, is the problem, since shorter windows surface results sooner.
- ✓
Increase the number of Streaming Units (SUs)
Why this is correct
Sustained SU utilisation at or above 90% means the job is compute-bound, so adding Streaming Units partitions the query across more nodes and raises throughput. Other remedies, such as rewriting the query or increasing partition count, do not relieve the saturated compute capacity.
- ✗
Decrease the event ordering tolerance
Why it's wrong here
Event ordering tolerance controls how long late events are buffered before processing; shortening it reduces latency tolerance and can drop late data, but does nothing to relieve saturated Streaming Units. It is tempting when tuning temporal correctness for out-of-order telemetry, where tighter tolerance is a deliberate latency-versus-completeness trade-off.
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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
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.