DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Which THREE metrics should you monitor to evaluate the performance of an Azure Stream Analytics job?
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
✓
Input Events Backlogged
Input Events Backlogged (A) is a correct metric because it measures the number of input events that are waiting to be processed, directly revealing whether the job is falling behind its input rate. Output Events (B) is correct because it counts the events emitted to the output sink, letting you verify the job is actually producing results and detect drops in throughput. Watermark Delay (seconds) (E) is correct because it quantifies how far behind real time the job is running, which is the key indicator of streaming latency and timeliness. Conversion Errors (C) is not one of the three performance metrics for evaluating job throughput/latency; it reflects data-format deserialization problems rather than performance. SU (Memory) Utilization (D) is a Streaming Unit resource metric that indicates capacity consumption, not the job's runtime performance as measured by backlog, output, and watermark delay.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Input Events Backlogged
Why this is correct
Input Events Backlogged counts events awaiting processing, exposing whether the job's streaming units or query logic cannot keep pace with incoming throughput. Rising backlog directly signals a performance bottleneck in the Azure Stream Analytics pipeline.
- ✓
Output Events
Why this is correct
Output Events counts records written downstream, directly exposing sink throughput and backpressure. Since the stem asks for performance metrics of a Stream Analytics job, this reveals whether the job keeps pace with input volume or stalls at the destination, satisfying the requirement to evaluate end-to-end processing performance.
- ✗
Conversion Errors
Why it's wrong here
Conversion Errors counts malformed or deserialisation failures in input events, indicating data-quality problems rather than job performance. It would be correct when diagnosing schema mismatches or corrupt payloads, not for evaluating throughput, latency, or resource pressure.
- ✗
SU (Memory) Utilization
Why it's wrong here
SU (Memory) Utilisation measures streaming-unit memory consumption, which is a capacity and cost signal rather than a performance indicator of the job's processing behaviour. It would be correct when right-sizing streaming units to avoid memory throttling.
- ✓
Watermark Delay (seconds)
Why this is correct
Watermark Delay directly measures how far the job's event-time progress lags behind wall-clock time, exposing backlog and processing bottlenecks. For a Stream Analytics job, this metric satisfies the requirement to evaluate performance, since sustained high delay indicates the job cannot keep pace with incoming throughput.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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