DP-203 Develop data processing Practice Question
You have an Azure Data Factory pipeline that copies data from an on-premises SQL Server to Azure Blob Storage. The pipeline fails intermittently with timeout errors. You need to improve reliability. What should you do?
⚠ Common exam trap
Watch out — candidates often confuse high availability of the integration runtime (Option A) with fault tolerance of the copy activity, not realizing that HA only protects the IR nodes, not the data transfer itself.
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 fault tolerance and use staging
Enabling fault tolerance with staging in Azure Data Factory allows the copy activity to automatically retry transient failures (such as timeout errors) by staging intermediate data in Azure Blob Storage. This mechanism uses a two-phase commit approach: data is first written to a staging location, and then committed to the final sink only after successful validation, which isolates the pipeline from intermittent source or sink failures.
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 a self-hosted integration runtime with high availability
Why it's wrong here
This helps with IR availability but not directly with copy activity timeouts.
- ✓
Enable fault tolerance and use staging
Why this is correct
Fault tolerance allows the copy activity to retry on transient errors, and staging improves performance and reliability.
- ✗
Change the source to Azure SQL Database
Why it's wrong here
This changes the requirement, not fix the reliability.
- ✗
Increase the degree of copy parallelism
Why it's wrong here
This can exacerbate timeout issues by overwhelming the source.
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
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.
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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.