DP-203 Develop data processing Practice Question
You have an Azure Stream Analytics job that reads from an Event Hub and writes to Azure SQL Database. The job processes high-velocity IoT sensor data. You notice that the output to SQL Database is slower than expected and the job's watermark delay is increasing. What should you do to improve throughput?
⚠ Common exam trap
Watch out — candidates often assume increasing compute resources (Streaming Units) always solves performance issues, but they overlook that the bottleneck is frequently at the output sink, requiring architectural changes like partitioning rather than scaling.
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
✓
Partition the output by a column like DeviceId.
Partitioning the output by a column like DeviceId allows Azure Stream Analytics to write to multiple SQL Database tables or use partitioned tables, enabling parallel writes. This reduces contention and improves throughput because the job can distribute the load across multiple write operations, directly addressing the bottleneck caused by high-velocity IoT sensor data overwhelming a single output stream.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Partition the output by a column like DeviceId.
Why this is correct
Partitioning allows parallel writes to SQL.
- ✗
Disable late arrival and out-of-order event handling.
Why it's wrong here
May cause data loss; does not improve throughput.
- ✗
Increase the Streaming Units (SU) of the job.
Why it's wrong here
May help but partitioning is more effective for SQL output.
- ✗
Decrease the window size in the query.
Why it's wrong here
Smaller windows increase frequency of writes, potentially worsening the issue.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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