mediumMultiple Choice
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 natively captures Data Factory pipeline run outcomes and duration metrics, so alert rules trigger on failures or threshold breaches without extra infrastructure. This satisfies the minimise cost and operational overhead constraint, since no custom logging, storage or polling code is required.
- ✗
Send all pipeline run logs to Log Analytics and create alert rules based on custom log searches.
Why it's wrong here
Log Analytics ingestion and custom log searches incur workspace and query costs, and require maintaining KQL rules. It is tempting for rich correlation, but Data Factory already emits native metric alerts for failures and duration thresholds, meeting the low-cost, low-overhead requirement.
- ✗
Create an Azure Logic App that runs every minute to check pipeline run status via REST API.
Why it's wrong here
Logic Apps polling the REST API every minute incurs per-action charges and needs custom error-handling code, whereas ADF's native metric alerts on failed runs and duration thresholds trigger directly with no polling infrastructure. Polling suits scenarios needing cross-service orchestration or custom workflow logic beyond ADF's built-in alerting.
Go deeper
Related to this question
About these practice questions
One of 509 original DP-203 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.