DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are monitoring an Azure Data Factory pipeline that runs hourly. You notice that the pipeline has been failing intermittently with an error indicating 'Activity timeout'. Which Azure Monitor metric should you set an alert on to proactively detect such failures?
⚠ Common exam trap
DP-203 often tests the distinction between metrics that indicate performance issues versus those that directly signal failures, so candidates must know that 'Failed pipeline runs' is the specific metric for failure detection, not queue depth or duration.
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
✓
Failed pipeline runs metric
The 'Failed pipeline runs' metric in Azure Monitor directly tracks the number of pipeline runs that have failed, including failures caused by activity timeouts. Setting an alert on this metric allows you to be notified immediately when a pipeline run fails, enabling proactive detection. Other metrics like queue depth or duration may indicate potential issues but do not directly signal a failure. Therefore, the correct metric to alert on for detecting failures is 'Failed pipeline runs'.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Integration runtime queue depth metric
Why it's wrong here
Queue depth measures how many activity runs wait for an available integration runtime node, so it stays low while a single long-running activity times out internally. It is tempting because queue depth genuinely diagnoses throttling and insufficient concurrent IR capacity, not per-activity execution overruns.
- ✗
Pipeline duration metric
Why it's wrong here
Pipeline duration measures total wall-clock time of the whole pipeline run, which can stay within bounds while an individual activity exceeds its own timeout setting. It is tempting because it genuinely detects slow end-to-end pipeline runs, but the alert must target the activity-level duration metric instead.
- ✗
Data read and data written metrics
Why it's wrong here
Data read and data written count bytes moved by activities; they describe throughput volume, not elapsed execution time, so a hung activity can time out with normal byte counts. They are tempting because they genuinely diagnose unexpected data volumes or movement stalls, not timeout conditions.
- ✓
Failed pipeline runs metric
Why this is correct
The Failed pipeline runs metric counts completed runs that ended in failure, capturing activity timeouts as they occur. Alerting on it detects the intermittent failures proactively, rather than waiting for individual activity-level errors to surface.
Go deeper
Related to this question
Learn chapter
Monitor Data Storage and Processing
Key term
Azure Data Factory
Azure Data Factory is a cloud-based data integration service that lets you create, schedule, and orchestrate data pipelines to move and transform data from various sources to destinations.
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.