easyMultiple ChoiceObjective-mapped
DP-203 Practice Question: Tuning an Azure Stream Analytics job that reads…
You are tuning an Azure Stream Analytics job that reads from an Event Hub and writes to an Azure Synapse Analytics table. The job's SU% utilization is consistently at 90%. Which action would most likely reduce the SU% utilization?
⚠ Common exam trap
Candidates often confuse scaling the input source (Event Hub throughput units) or optimizing the output sink (partitioning) with directly addressing the compute bottleneck, but only increasing SUs reduces the compute utilization percentage.
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) allocated to the job.
Increasing the number of streaming units (SU) allocated to the job directly adds more compute resources, which reduces the SU% utilization by distributing the workload across more SUs. Since the job is consistently at 90% utilization, adding SUs lowers the per-SU load, preventing throttling and improving throughput. This is the standard scaling approach for Azure Stream Analytics when SU% is high.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the Event Hub throughput units.
Why it's wrong here
Reducing input rate may lower utilization but also reduces throughput, which is not an optimization goal.
- ✗
Partition the output table in Azure Synapse Analytics.
Why it's wrong here
Output partitioning improves write performance but does not affect the streaming job's processing utilization.
- ✗
Use a reference data join to filter events.
Why it's wrong here
Reference data joins can increase processing complexity and utilization.
- ✓
Increase the number of streaming units (SU) allocated to the job.
Why this is correct
More SUs provide additional compute resources, lowering the utilization percentage for the same workload.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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.
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.
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