DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are monitoring Azure Data Factory pipelines. You need to set up an alert when a pipeline run fails. What is the most efficient way to achieve this?
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
✓
Create an Azure Monitor alert based on the 'Failed pipeline runs' metric.
The most efficient way to alert on pipeline failures is to use the 'Failed pipeline runs' metric in Azure Monitor, as it is a native metric provided by Azure Data Factory without additional configuration. Option A (logging to custom table) requires extra activities and setup. Option B (diagnostic settings and log alerts) is possible but more complex than using the metric. Option D (Application Insights) is not directly integrated for pipeline run monitoring.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add an activity in each pipeline to log failures to a custom table in Log Analytics.
Why it's wrong here
This requires modifying every pipeline.
- ✗
Enable diagnostic settings to send pipeline runs to a Log Analytics workspace and create a log alert.
Why it's wrong here
Enabling diagnostic settings to send logs to a Log Analytics workspace is not the most efficient approach for simply alerting on pipeline run failures. Azure Data Factory emits platform metrics, such as "Pipeline Failed Runs", which can directly trigger an Azure Monitor metric alert, bypassing the need for log ingestion and KQL queries. This option is tempting because Log Analytics provides powerful, centralised log analysis and customisable alerting for complex scenarios, such as correlating failures with specific error messages or other system events, making it ideal for comprehensive monitoring beyond basic success/failure notifications.
- ✓
Create an Azure Monitor alert based on the 'Failed pipeline runs' metric.
Why this is correct
This is the simplest method using built-in metrics.
- ✗
Use Application Insights to monitor pipeline executions.
Why it's wrong here
Data Factory does not directly integrate with Application Insights for pipeline runs.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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