Question 791 of 760
DP-203 Develop data processing Practice Question
You need to perform incremental data loading from Azure SQL Database to Azure Data Lake Storage Gen2 using Azure Data Factory. Which approach is the most efficient?
⚠ Common exam trap
Microsoft often tests the misconception that any trigger-based or full-load approach can be adapted for incremental loading, but the key is minimizing data movement; candidates may overlook the watermark pattern and choose a full-load option because they think deduplication later solves the problem.
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 retrieve the last watermark value, then copy only new records with a filter.
It uses a lookup activity to retrieve the last watermark value (e.g., a timestamp or incrementing key), then copies only new or changed records via a filter in the Copy activity. This minimizes data movement and processing time, making it the most efficient incremental loading approach 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.
- ✓
Use a lookup activity to retrieve the last watermark value, then copy only new records with a filter.
Why this is correct
Watermark pattern is efficient and well-supported.
- ✗
Use a tumbling window trigger with a data flow that processes all data each time.
Why it's wrong here
Tumbling window triggers run on schedule but still process all data if not filtered.
- ✗
Use a mapping data flow with a full load and then use Azure Databricks to deduplicate.
Why it's wrong here
Adding Databricks for deduplication is unnecessary complexity.
- ✗
Copy the entire table every time and use Azure Synapse serverless SQL to filter duplicates.
Why it's wrong here
Full copy is resource-intensive and slow.
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Last reviewed: Jun 30, 2026
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