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?
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 directly captures the number of pipeline runs that have failed, including those due to timeout errors, making it the most appropriate metric for alerting on such failures. Option A is incorrect because 'Integration runtime queue depth' indicates how many activities are queued for execution, not failures. Option B is incorrect because 'Pipeline duration' shows how long the pipeline runs, but a timeout is a specific failure type that would be captured by failed runs. Option C is incorrect because 'Data read and data written' measure data throughput and are unrelated to pipeline failures.
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 is for self-hosted IR performance, not pipeline failures.
- ✗
Pipeline duration metric
Why it's wrong here
Duration alone does not indicate failure; a timeout is a specific failure.
- ✗
Data read and data written metrics
Why it's wrong here
These metrics measure throughput, not failures.
- ✓
Failed pipeline runs metric
Why this is correct
This metric increments each time a pipeline run fails, allowing proactive alerting.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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.
About these practice questions
Courseiva writes every DP-203 question from scratch — 760 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.