easyMultiple Choice
DP-203 Practice Question: A data engineer monitors an Azure Stream…
A data engineer monitors an Azure Stream Analytics job that processes real-time data. The job is falling behind, and the SU utilization is at 100%. Which action should be taken to improve performance?
⚠ Common exam trap
It's easy for candidates to think reducing SU or splitting the job is a valid optimization, but the correct response is to increase SU when utilization is at 100%, as this directly addresses the resource bottleneck.
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 (SU).
When SU utilization reaches 100%, the job is fully saturated and cannot process incoming data fast enough. Increasing the number of Streaming Units (SU) allocates more compute resources (CPU and memory) to the job, allowing it to handle higher throughput and reduce backlog. This is the direct and recommended action for resolving performance bottlenecks caused by insufficient SU capacity.
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 number of Streaming Units (SU).
Why this is correct
Raising Streaming Units directly addresses the saturated compute capacity: each SU bundles CPU and memory, so adding SUs partitions the query across more nodes, relieving the 100% utilisation bottleneck. This satisfies the stem's constraint that the job is falling behind solely because existing SU allocation is exhausted.
- ✗
Reduce the number of Streaming Units.
Why it's wrong here
Reducing Streaming Units lowers the compute allocated to the job, so at 100% SU utilisation it worsens the backlog. It is tempting as a cost-saving measure, and would be correct when SU utilisation is low and the job is over-provisioned, not when it is saturated.
- ✗
Change the query compatibility level to 1.0.
Why it's wrong here
Compatibility level governs query language semantics, not compute capacity, so it cannot relieve 100% SU utilisation. It is tempting because level changes can improve some query behaviours, and would be correct when migrating legacy syntax, not when the job is resource-bound.
- ✗
Deploy a second Stream Analytics job and split the input.
Why it's wrong here
Splitting input across a second job adds coordination overhead and does not increase the compute available to the existing saturated job. It is tempting because horizontal scaling works for many services, and would be correct for partitioning independent workloads, not for relieving a single job's SU ceiling.
Visual reference
Go deeper
Related to this question
About these practice questions
Courseiva writes every DP-203 question from scratch — 509 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.