DP-203 Develop data processing Practice Question
Your company runs a streaming job in Azure Stream Analytics that ingests data from Event Hubs and outputs to Azure Synapse Analytics. The job is failing with a 'Watermark delay' alert and the output to Synapse is delayed by over 30 minutes. The input rate is 5,000 events per second. The job uses a 1-minute tumbling window. What is the most likely cause of the delay?
⚠ Common exam trap
A common mix-up: candidates confuse a watermark delay alert with late-arriving events (Option B), but the alert indicates the job is falling behind overall, not just handling late data, and the 30-minute delay points to insufficient compute resources rather than data timing issues.
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
✓
The Stream Analytics job is under-provisioned in terms of Streaming Units (SUs).
A watermark delay alert in Azure Stream Analytics indicates that the job is falling behind in processing incoming data. With an input rate of 5,000 events per second and a 1-minute tumbling window, the job requires sufficient Streaming Units (SUs) to keep up. Under-provisioned SUs cause backpressure, leading to output delays exceeding 30 minutes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The output schema in Synapse does not match the Stream Analytics output.
Why it's wrong here
A schema mismatch causes output write failures or job errors, not a steady 30-minute watermark delay with successful ingestion. Aligning output schemas is correct when Synapse rejects rows, but here the bottleneck is streaming unit capacity or partitioning, which throttles processing.
- ✗
The Event Hubs has a large number of late-arriving events.
Why it's wrong here
Late-arriving events affect the late-arrival tolerance and watermark progression only marginally at 5,000 events per second; the delay is sustained, not event-timing related. Late-arrival policies are correct when timestamps skew beyond the tolerance window, not for a 30-minute output backlog.
- ✗
The tumbling window size is too large.
Why it's wrong here
A one-minute tumbling window is short, so window size is not the bottleneck; larger windows aggregate more events and can actually reduce output volume. Tumbling windows suit fixed-interval aggregation, but the watermark delay stems from processing throughput or event backlog, not window duration.
- ✓
The Stream Analytics job is under-provisioned in terms of Streaming Units (SUs).
Why this is correct
Insufficient Streaming Units cap the job's processing throughput, so it cannot keep pace with 5,000 events per second. The backlog grows, inflating watermark delay and delaying Synapse output beyond 30 minutes. Scaling SUs raises parallel processing capacity, clearing the bottleneck and restoring timely windowed output.
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Implement Azure Synapse Analytics
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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