- A
InputEvents and OutputEvents metrics
InputEvents and OutputEvents (Option A) are correct. By comparing these two metrics, you can see if every event entering the Stream Analytics job is being output, indicating potential data loss if they diverge.
- B
Duration metric
Why wrong: Duration (Option B) is a metric for job runs, not streaming throughput. It does not directly indicate data loss or processing delays.
- C
Data read and data written metrics
Why wrong: Data read and data written metrics are not defined for Azure Stream Analytics; they apply to other services like copy activities in data integration.
- D
Pipeline run count metric
Why wrong: Pipeline run count metric is relevant for batch pipeline services, not for Stream Analytics streaming jobs.
- E
Backlogged input events metric
Backlogged input events (Option E) directly measures the number of events waiting to be processed; a high or growing value signals processing delays.
DP-203 InputEvents Practice Question
This DP-203 practice question tests your understanding of secure, monitor, and optimize data storage and data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: inputEvents. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are monitoring an Azure Stream Analytics job that processes streaming data from Event Hubs to Azure Synapse Analytics. Which TWO Azure Monitor metrics should you set alerts on to detect data loss or processing delays?
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
InputEvents and OutputEvents metrics
The correct metrics for detecting data loss or processing delays in an Azure Stream Analytics job are InputEvents and OutputEvents (Option A). By comparing these, you can determine if every event entering the job is being output; a mismatch indicates potential data loss. Backlogged input events (Option E) directly measures the number of events waiting to be processed, and a high or growing value signals processing delays. Option B (Duration) is not a direct indicator of data loss or processing delays in Stream Analytics. Option C (Data read/written) is not a metric for Stream Analytics. Option D (Pipeline run count) is not applicable to Stream Analytics streaming jobs.
Key principle: InputEvents
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
InputEvents and OutputEvents metrics
Why this is correct
InputEvents and OutputEvents (Option A) are correct. By comparing these two metrics, you can see if every event entering the Stream Analytics job is being output, indicating potential data loss if they diverge.
Related concept
InputEvents
- ✗
Duration metric
Why it's wrong here
Duration (Option B) is a metric for job runs, not streaming throughput. It does not directly indicate data loss or processing delays.
- ✗
Data read and data written metrics
Why it's wrong here
Data read and data written metrics are not defined for Azure Stream Analytics; they apply to other services like copy activities in data integration.
- ✗
Pipeline run count metric
Why it's wrong here
Pipeline run count metric is relevant for batch pipeline services, not for Stream Analytics streaming jobs.
- ✓
Backlogged input events metric
Why this is correct
Backlogged input events (Option E) directly measures the number of events waiting to be processed; a high or growing value signals processing delays.
Related concept
InputEvents
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- InputEvents
- OutputEvents
- Backlogged input events
- Metric alert
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
InputEvents
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. InputEvents Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Visual reference
What to study next
Got this wrong? Here's your next step.
Review inputEvents, then practise related DP-203 questions on the same topic to reinforce the concept.
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Secure, monitor, and optimize data storage and data processing — study guide chapter
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FAQ
Questions learners often ask
What does this DP-203 question test?
Secure, monitor, and optimize data storage and data processing — This question tests Secure, monitor, and optimize data storage and data processing — InputEvents.
What is the correct answer to this question?
The correct answer is: InputEvents and OutputEvents metrics — The correct metrics for detecting data loss or processing delays in an Azure Stream Analytics job are InputEvents and OutputEvents (Option A). By comparing these, you can determine if every event entering the job is being output; a mismatch indicates potential data loss. Backlogged input events (Option E) directly measures the number of events waiting to be processed, and a high or growing value signals processing delays. Option B (Duration) is not a direct indicator of data loss or processing delays in Stream Analytics. Option C (Data read/written) is not a metric for Stream Analytics. Option D (Pipeline run count) is not applicable to Stream Analytics streaming jobs.
What should I do if I get this DP-203 question wrong?
Review inputEvents, then practise related DP-203 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
InputEvents
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Last reviewed: Jun 21, 2026
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