DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your company uses Azure Data Factory to orchestrate data movement. You need to monitor pipeline runs across multiple factories and create a dashboard that shows success and failure rates over the past 30 days. What is the most efficient approach?
⚠ Common exam trap
DP-203 often tests whether candidates know that Data Factory monitoring UI is per-factory and that cross-factory aggregation requires diagnostic settings to Log Analytics — many pick the UI option because it 'looks' like monitoring.
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
✓
Configure diagnostic settings for each Data Factory to send logs to a Log Analytics workspace, then create a workbook using KQL queries.
Diagnostic settings in Azure Data Factory can route ActivityRuns, PipelineRuns, TriggerRuns, and other logs to a Log Analytics workspace. Once logs from multiple factories land in the same workspace, KQL queries in an Azure Monitor workbook can aggregate success/failure counts across all factories over a 30-day window, giving a single consolidated dashboard. This is the most efficient, scalable approach for multi-factory 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.
- ✗
Use the Data Factory monitoring UI to view runs for each factory individually.
Why it's wrong here
The per-factory UI shows one factory at a time and retains limited history, so it cannot aggregate success and failure rates across factories for 30 days. It would be the choice for ad-hoc troubleshooting of a single pipeline run in one factory.
- ✗
Enable Azure Storage Analytics and query the logs stored in a storage account.
Why it's wrong here
Storage Analytics logs only blob, table, queue and file transactions within one storage account; it records nothing about pipeline activity, so no run history exists to query. It is tempting because ADF does write activity logs to storage, but Azure Monitor metrics and Log Analytics are the actual mechanism for cross-factory run monitoring.
- ✓
Configure diagnostic settings for each Data Factory to send logs to a Log Analytics workspace, then create a workbook using KQL queries.
Why this is correct
Routing diagnostic logs from every factory into a single Log Analytics workspace centralises cross-factory data, and a workbook built on KQL queries aggregates success and failure rates over 30 days. This satisfies the multi-factory dashboard requirement without per-factory tooling.
- ✗
Create alert rules in Azure Monitor for each pipeline failure and aggregate manually.
Why it's wrong here
Alert rules fire per failure and hold no historical success-rate aggregation, so a 30-day dashboard cannot be built from them without external processing. Alerts suit real-time notification of specific conditions, not trend reporting across factories.
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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.