easyMultiple Choice
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 Event Hub throughput units throttles ingress, which can cause backlog and does not lower the query's SU consumption; the job still processes the same events. It is tempting because scaling Event Hub capacity controls ingestion cost, but that suits managing Event Hub throughput limits, not reducing Stream Analytics SU%.
- ✗
Partition the output table in Azure Synapse Analytics.
Why it's wrong here
Partitioning the Synapse output table changes storage layout and query pruning, not the streaming compute consumed by the job's query steps. It is tempting because partitioning genuinely improves Synapse write and read performance, but it would be the right choice when sink latency or query cost, rather than SU% consumption, is the bottleneck.
- ✗
Use a reference data join to filter events.
Why it's wrong here
Adding a reference data join introduces an extra join operation and reference data refresh, increasing rather than reducing SU consumption. It is tempting because reference joins genuinely enrich and filter streaming events, but that suits scenarios needing lookup-based enrichment, not lowering the job's streaming unit utilisation.
- ✓
Increase the number of streaming units (SU) allocated to the job.
Why this is correct
Streaming units represent the compute and memory capacity assigned to the job. Raising the SU count distributes the same query workload across more parallel processing nodes, directly lowering the percentage of allocated capacity consumed and relieving the 90% saturation.
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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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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