DP-203 Develop data processing Practice Question
You are building a data pipeline in Azure Data Factory to copy data from an on-premises SQL Server database to Azure Blob Storage. The pipeline must run daily and handle incremental updates. The on-premises SQL Server table has a LastModifiedDate column that is updated when a row changes. What is the most efficient way to implement incremental loads?
⚠ Common exam trap
The trap here is that candidates often overcomplicate the solution by choosing CDC (Option A) or full loads (Options C and D), when a simple watermark-based query using the existing LastModifiedDate column is the most efficient and straightforward approach for incremental loads in Azure Data Factory.
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
✓
Use a Lookup activity to get the maximum LastModifiedDate from the destination, then use a Copy activity with a query that filters rows where LastModifiedDate > that value.
It uses a Lookup activity to retrieve the maximum LastModifiedDate from the destination (Azure Blob Storage), then passes that value as a parameter to a Copy activity that queries only rows where LastModifiedDate exceeds it. This minimizes data transfer by reading only new or changed rows, and it avoids the overhead of enabling Change Data Capture or performing full loads, making it the most efficient approach for incremental loads in Azure Data Factory.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Change Data Capture (CDC) on the SQL Server database and use an ADF mapping data flow to read changes.
Why it's wrong here
CDC is overkill for a simple incremental load with a timestamp column.
- ✓
Use a Lookup activity to get the maximum LastModifiedDate from the destination, then use a Copy activity with a query that filters rows where LastModifiedDate > that value.
Why this is correct
This is the standard watermark pattern for incremental loads.
- ✗
Use a tumbling window trigger with a window size of 1 day and copy all data from the source each time.
Why it's wrong here
Tumbling window triggers schedule but do not implement incremental logic.
- ✗
Perform a full load every day and use a Delete activity to remove duplicates.
Why it's wrong here
Full loads are inefficient and costly.
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
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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