DP-203 Develop data processing Practice Question
You have an Azure Data Factory pipeline that uses a Copy activity to move data from an on-premises SQL Server to Azure Blob Storage. The pipeline fails intermittently with a timeout error. You need to improve the reliability of the data transfer. Which configuration change should you make?
⚠ Common exam trap
Many candidates confuse 'fault tolerance' with 'retry policy,' assuming that increasing retries is the only way to handle failures, whereas fault tolerance addresses row-level errors that cause timeouts without requiring a full activity restart.
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 configure skip incompatible rows.
Enabling fault tolerance and configuring 'skip incompatible rows' allows the Copy activity to continue processing even when some rows cause errors (e.g., type conversion failures), which can manifest as timeouts when the activity repeatedly retries the same problematic rows. This setting improves reliability by skipping rows that cannot be copied, preventing the entire pipeline from failing on intermittent data issues.
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 staged copy with an intermediate Azure Blob Storage.
Why it's wrong here
Staged copy is for large data volumes and network efficiency, not for error handling.
- ✗
Use PolyBase as the sink.
Why it's wrong here
PolyBase is not applicable for copying to Blob Storage.
- ✓
Enable fault tolerance and configure skip incompatible rows.
Why this is correct
This allows the copy to continue even if some rows fail, improving reliability.
- ✗
Increase the retry count in the pipeline activity.
Why it's wrong here
Retry may help transient failures but does not address incompatible data errors.
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
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