Quick Answer
The answer is Change Data Capture (CDC) in Azure Data Factory. This feature is the correct choice for incremental loads because it efficiently identifies insert, update, and delete operations from source data, allowing you to load only new or changed rows rather than performing a full refresh. For Azure Blob Storage CSV files, CDC is typically implemented using a watermark column like LastModifiedDate, which tracks row-level changes and reduces both latency and resource consumption. On the DP-203 exam, this question tests your understanding of incremental loading patterns versus full loads; a common trap is confusing CDC with the Copy Data tool’s native delta copy, but CDC is the broader feature designed for change tracking across heterogeneous sources. Remember the memory tip: “CDC catches changes, watermarks mark the moment”—if you see a scenario requiring daily batch processing with CSV files in Blob Storage, think CDC with a timestamp-based watermark to isolate new rows.
This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You are designing a batch processing solution in Azure Data Factory. The source is an Azure Blob Storage container with CSV files. The target is an Azure SQL Database. The pipeline must run daily and incrementally load only new or changed rows. Which Data Factory feature should you use?
Mapping Data Flow with a watermark column
Why wrong: Data Flow is for transformations, not for CDC.
Stored Procedure activity to run a merge statement
Why wrong: Stored Procedure alone does not detect changes.
Copy activity with a full load every time
Why wrong: Full load is not incremental.
Change data capture (CDC) resource
CDC enables incremental loading by tracking changes.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Change data capture (CDC) resource
Option D is correct because Change Data Capture (CDC) in Azure Data Factory enables incremental loading by capturing insert, update, and delete operations from the source. For Azure Blob Storage CSV files, CDC can be implemented using the 'LastModifiedDate' or a watermark column to identify new or changed rows, avoiding full reloads and reducing latency.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Mapping Data Flow with a watermark column
Why it's wrong here
Data Flow is for transformations, not for CDC.
Stored Procedure activity to run a merge statement
Why it's wrong here
Stored Procedure alone does not detect changes.
Copy activity with a full load every time
Why it's wrong here
Full load is not incremental.
Change data capture (CDC) resource
Why this is correct
CDC enables incremental loading by tracking changes.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse CDC with a full load or assume a stored procedure can handle incremental logic without source-side change tracking, missing that CDC is the only native incremental load feature for Blob Storage in Azure Data Factory.
Detailed technical explanation
Azure Data Factory's CDC capability for Blob Storage relies on the 'LastModifiedDate' property of blobs or a custom watermark column in the CSV files. Under the hood, ADF uses a watermark query to compare the last run timestamp with the current file timestamps, copying only files modified after the last pipeline run. In real-world scenarios, this is critical for large datasets where full loads would exceed SLAs or incur high costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: Change data capture (CDC) resource — Option D is correct because Change Data Capture (CDC) in Azure Data Factory enables incremental loading by capturing insert, update, and delete operations from the source. For Azure Blob Storage CSV files, CDC can be implemented using the 'LastModifiedDate' or a watermark column to identify new or changed rows, avoiding full reloads and reducing latency.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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