easyMultiple Choice
DP-203 Practice Question: A company runs a streaming pipeline using Azure…
A company runs a streaming pipeline using Azure Stream Analytics to ingest IoT data and output to Azure SQL Database. They notice that the output latency increases over time and eventually the job fails with a timeout error. What is the most likely cause?
⚠ Common exam trap
The trap here is that candidates often attribute output latency to input-side issues like partitioning or consumer groups, but the symptom of increasing latency over time points to a downstream bottleneck, specifically missing indexes on the SQL target table.
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 Azure SQL Database target table lacks proper indexes.
The most likely cause is that the Azure SQL Database target table lacks proper indexes. Without indexes, each batch of output from Stream Analytics triggers full table scans for inserts or updates, causing cumulative latency. Over time, the backlog exceeds the job's timeout threshold (default 5 minutes for output), leading to failure.
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 Stream Analytics job has a high late arrival tolerance.
Why it's wrong here
Late arrival tolerance affects how long events wait for out-of-order data before processing; it does not accumulate output backlog or cause SQL write timeouts. It tempts because tolerance settings do influence latency, and would be the answer if the symptom were dropped or reordered events rather than steadily growing output delay.
- ✗
The event hub is not partitioned correctly.
Why it's wrong here
Partitioning governs parallel throughput and ordering within the event hub, but a mispartitioned hub typically causes throttling or uneven ingestion, not progressive output latency ending in a SQL timeout. It tempts because partitioning is a common streaming bottleneck, and would be correct if ingestion itself were the failing stage.
- ✗
The event hub consumer group is misconfigured.
Why it's wrong here
A misconfigured consumer group would cause duplicate reads or startup failures, not steadily rising latency ending in timeout. The symptom points to output-side backpressure: Azure SQL Database throttling writes as the sink saturates. Consumer groups exist to let multiple readers each track their own position independently, which is a parallelism concern, not a sink-throughput one.
- ✓
The Azure SQL Database target table lacks proper indexes.
Why this is correct
Without suitable indexes, Azure SQL Database writes and lookups slow as the table grows, so Stream Analytics output batches queue and eventually exceed the query timeout. Adding indexes on the columns used by the sink's upsert or insert operations restores throughput and resolves the escalating latency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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