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DP-203 Practice Question: A company runs a mission-critical Azure Data…
A company runs a mission-critical Azure Data Factory pipeline that ingests data every hour from Azure Blob Storage into Azure Synapse Dedicated SQL Pool. Recently, the pipeline has been failing with timeout errors during the copy activity. The source blob files are around 500 MB each. Which configuration change would MOST effectively reduce the likelihood of timeout errors?
⚠ Common exam trap
A common mix-up: candidates assume increasing DIUs or decreasing batch size will solve timeout issues, but they fail to recognize that staging is specifically designed to handle large file transfers by breaking them into parallel chunks and providing built-in retry logic.
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 'Enable staging' and set 'Degree of copy parallelism' to a higher value.
Enabling staging allows the copy activity to use Azure Blob Storage as an intermediate staging area, which breaks the 500 MB files into manageable chunks and uses parallel staging writes to the Dedicated SQL Pool. This reduces the load on the single copy session and prevents timeout errors by leveraging the staging engine's retry and parallelization capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decrease the 'Batch size' for the copy activity.
Why it's wrong here
Smaller batches increase number of operations, potentially increasing timeout risk.
- ✗
Change the sink to use PolyBase with staging enabled.
Why it's wrong here
PolyBase is for bulk load into Synapse, not directly for timeout issues.
- ✗
Increase the Data Integration Unit (DIU) to 8.
Why it's wrong here
Higher DIU may not resolve timeout if the source is the bottleneck.
- ✓
Enable 'Enable staging' and set 'Degree of copy parallelism' to a higher value.
Why this is correct
Increases parallelism, reducing copy time and timeout likelihood.
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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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.