Question 177 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. You want to minimize cost and complexity. Which Azure Data Factory feature should you use?
⚠ Common exam trap
It's easy for candidates to confuse the 'Incremental copy' capability with manual watermark-based approaches (Option A) or assume that a Mapping Data Flow (Option D) is the only way to compare datasets, overlooking the native, cost-optimized change tracking integration.
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 the 'Incremental copy' capability with change tracking enabled on the source
Azure Data Factory's 'Incremental copy' capability with change tracking on Azure SQL Database automatically identifies and transfers only the changed rows since the last run, using the built-in change tracking mechanism. This minimizes cost and complexity by avoiding custom logic for watermark columns or full reloads, as it handles the delta extraction natively.
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 get the maximum timestamp from the sink and filter the source
Why it's wrong here
Requires custom logic and watermark table.
- ✓
Use the 'Incremental copy' capability with change tracking enabled on the source
Why this is correct
This is the simplest and most cost-effective method.
- ✗
Use a Stored Procedure activity to delete and reinsert data
Why it's wrong here
Full refresh is not incremental and wastes resources.
- ✗
Use a Mapping Data Flow to compare source and sink
Why it's wrong here
Mapping Data Flows perform full schema-aware transformations in-memory, but they lack the built-in watermark or change-tracking mechanisms required for incremental loading. This forces you to manually implement change detection logic, increasing both development effort and runtime cost. It is tempting because Data Flows excel at complex data cleansing and aggregation tasks, making them the correct choice when the primary need is heavy transformation rather than lightweight, cost-efficient delta ingestion.
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Last reviewed: Jun 24, 2026
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