DP-203 Develop data processing Practice Question
You are monitoring an Azure Stream Analytics job that processes data from an IoT hub. The job's output to Azure Synapse Analytics is experiencing high latency. The job's SU% utilization is at 90%. Which action will most likely reduce the latency?
⚠ Common exam trap
Test-takers frequently confuse output-side tuning (like partitioning or sink configuration) with the actual processing bottleneck, overlooking that high SU% utilization directly indicates the Stream Analytics job itself is the limiting factor.
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) allocated to the job.
The job's SU% utilization is at 90%, indicating that the current Streaming Units (SUs) are nearly saturated, causing a processing bottleneck. Increasing the number of SUs allocates more compute resources (CPU and memory) to the Stream Analytics job, allowing it to process incoming IoT data faster and reduce the latency to Azure Synapse Analytics. This directly addresses the high utilization issue, which is the most likely root cause of the latency.
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 (SUs) allocated to the job.
Why this is correct
More SUs provide more processing capacity, reducing latency.
- ✗
Decrease the watermark delay interval.
Why it's wrong here
Watermark delay controls event ordering, not processing speed.
- ✗
Increase the late arrival tolerance window.
Why it's wrong here
Late arrival tolerance affects how late events are handled, not latency.
- ✗
Increase the number of partitions in the output table.
Why it's wrong here
Partitioning helps with parallelism but not if the job itself is under-provisioned.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
About these practice questions
One of 760 original DP-203 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.