DP-203 Develop data processing Practice Question
You are monitoring an Azure Data Factory pipeline that copies data from Azure Blob Storage to Azure SQL Database. The pipeline fails intermittently with the error: 'Operation on target SQL table failed: String or binary data would be truncated.' Which action should you take to resolve this issue?
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 length of the destination columns in the SQL table to accommodate the source data.
The error indicates that source data length exceeds destination column length. Increasing column size resolves it. Option B is incorrect because the table already exists. Option C is incorrect because the error is not about connection. Option D is incorrect because the error is not about identity insert.
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 length of the destination columns in the SQL table to accommodate the source data.
Why this is correct
Direct fix for truncation error.
- ✗
Set 'enable identity insert' to true.
Why it's wrong here
Unrelated to truncation.
- ✗
Use auto-create table option in the copy activity.
Why it's wrong here
Auto-create would inherit source schema but may still truncate.
- ✗
Enable staging copy to use PolyBase.
Why it's wrong here
PolyBase does not fix truncation.
Visual reference
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
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