DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
Your Azure Data Factory pipeline uses a Self-Hosted Integration Runtime (SHIR) to copy data from an on-premises SQL Server to Azure Blob Storage. The copy activity is failing with a timeout error after 30 minutes. The data volume is 50 GB. You need to optimize the data transfer performance. Which configuration change should you make first?
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
✓
Increase the 'Degree of copy parallelism'
Increasing the 'Degree of copy parallelism' allows multiple threads to read from the source and write to the sink concurrently, significantly improving throughput for large datasets like 50 GB. Option B is incorrect because staging copy is used for scenarios like cross-cloud transfers or to leverage intermediate storage for performance, but it adds overhead and is not the first optimization for direct on-premises to Azure Blob copy. Option C is incorrect because increasing activity retry count only handles transient failures, not performance issues. Option D is incorrect because reducing Data Integration Units would decrease the resources available for the copy activity, worsening performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the 'Degree of copy parallelism'
Why this is correct
Parallelism improves throughput for large data
- ✗
Enable staging copy via Azure Blob Storage
Why it's wrong here
Staging is for cross-region or different stores, not performance
- ✗
Increase the 'Activity retry' count
Why it's wrong here
Retries do not improve performance
- ✗
Reduce the 'Data Integration Unit' (DIU) setting
Why it's wrong here
Reducing DIU would decrease performance
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
Introduction to Azure Data Engineering
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.
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.
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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.