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
SU% utilisation at 90% means the job is near its allocated streaming capacity, so processing falls behind the IoT Hub input rate and output latency grows. Adding Streaming Units provides more compute parallelism, allowing the backlog to drain faster.
- ✗
Decrease the watermark delay interval.
Why it's wrong here
Watermark delay controls the trade-off between result latency and completeness of out-of-order events; lowering it reduces buffering but does not relieve saturated compute. It would be right when the job waits too long before emitting results, yet 90% SU indicates the bottleneck is processing capacity.
- ✗
Increase the late arrival tolerance window.
Why it's wrong here
Late arrival tolerance governs how long events may be reordered before processing, affecting correctness of results rather than throughput. It would be correct when events arrive out of order and are being dropped, but the 90% SU utilisation points to compute saturation, not event timing.
- ✗
Increase the number of partitions in the output table.
Why it's wrong here
Partition count in the Synapse output table governs parallel write throughput, not the streaming compute that is saturated at 90% SU. It would help when a single partition bottlenecks ingestion, but here the constraint is SU capacity, so scaling streaming units addresses the latency.
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Develop Stream Processing Solutions
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.
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