DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are monitoring an Azure Data Factory pipeline that copies data from an on-premises SQL Server to Azure Blob Storage. The pipeline occasionally fails with a timeout error. You need to identify the cause of the failures and receive proactive alerts when similar issues occur. What should you do?
⚠ Common exam trap
The trap here is focusing on performance tuning or automatic retries to address timeouts, while overlooking that the core requirement is to diagnose the cause and get alerted, which is achieved through monitoring and alerts.
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
✓
Enable diagnostic settings to send logs to Azure Monitor and create an alert rule on the pipeline failure metric.
Diagnostic settings in Azure Data Factory can route detailed logs and metrics to Azure Monitor, enabling analysis of pipeline failures. Creating an alert rule on failure metrics ensures you are notified proactively. This combination provides both root-cause investigation and ongoing monitoring. Other options either mask failures, improve performance without visibility, or offer generic recommendations.
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 a tumbling window trigger with retry policy.
Why it's wrong here
A tumbling window trigger with retries can automatically retry failed pipeline runs, which might mitigate transient timeouts, but it does not help you identify the root cause or provide proactive alerts. Retries mask the issue and do not deliver diagnostic insights. The scenario requires understanding the failure cause and receiving alerts, not just automatic recovery.
- ✗
Use Azure Advisor recommendations for Data Factory.
Why it's wrong here
Azure Advisor provides best-practice recommendations, such as cost or performance optimizations, but it does not deliver real-time monitoring of pipeline runs or proactive alerts on failures. It is not a diagnostic tool for specific pipeline errors. Thus, it cannot help identify the timeout cause or notify you when similar failures happen.
- ✗
Increase the DIU count for the copy activity.
Why it's wrong here
Increasing Data Integration Units (DIUs) can improve copy throughput and may reduce timeouts caused by insufficient parallelism. However, it does not provide visibility into the failure cause or enable alerting. Without monitoring and alerts, you would not know if the change resolved the issue or if new failures occur. The requirement includes proactive alerting, which this option lacks.
- ✓
Enable diagnostic settings to send logs to Azure Monitor and create an alert rule on the pipeline failure metric.
Why this is correct
Enabling diagnostic settings streams pipeline run logs and metrics to Azure Monitor, where you can analyze failures and create alert rules based on metrics like PipelineFailedRuns. This provides both the diagnostic data to identify the timeout cause and proactive notifications. It directly addresses the need to monitor and alert on failures without manual intervention.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Optimize Data Storage Performance
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.
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.
About these practice questions
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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.