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DP-203 Practice Question: A data engineer is designing a monitoring…
A data engineer is designing a monitoring solution for Azure Data Factory pipelines. They need to be alerted when a pipeline run fails or when the duration exceeds a threshold. The solution must minimize cost and operational overhead. Which approach should they use?
⚠ Common exam trap
The trap here is that candidates over-engineer the solution by choosing event-driven or log-based approaches (A, C, D) when the simplest, most cost-effective native monitoring (Azure Monitor alerts) is available, often forgetting that Data Factory emits metrics and activity logs by default without additional setup.
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
✓
Use Azure Monitor metrics and activity logs to create alert rules for pipeline failures and duration.
Azure Monitor provides native, cost-effective alerting for Azure Data Factory pipelines using metrics (e.g., pipeline run duration) and activity logs (e.g., pipeline run failures). This approach requires no additional compute or log ingestion costs, as alerts are configured directly on the resource's monitoring data, minimizing both cost and operational overhead.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure Azure Event Grid to send pipeline run events to Azure Functions for alerting.
Why it's wrong here
Event Grid with Azure Functions introduces unnecessary complexity and cost for simple pipeline failure and duration alerts, as it requires custom code development and function execution charges. The correct approach uses Data Factory’s native integration with Azure Monitor and alert rules, which directly evaluate pipeline run status and duration metrics without additional services. This option is tempting because Event Grid excels at real-time event-driven workflows, such as triggering downstream processing on pipeline completion, but it is over-engineered for basic threshold-based alerting.
- ✓
Use Azure Monitor metrics and activity logs to create alert rules for pipeline failures and duration.
Why this is correct
Azure Monitor provides built-in metrics and alerts for Azure Data Factory with minimal cost.
- ✗
Send all pipeline run logs to Log Analytics and create alert rules based on custom log searches.
Why it's wrong here
This incurs additional data ingestion costs and complexity.
- ✗
Create an Azure Logic App that runs every minute to check pipeline run status via REST API.
Why it's wrong here
Polling is inefficient and increases operational overhead.
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