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
A watermark lookup captures the last-loaded value, and the copy activity's filter predicate then transfers only rows exceeding it, avoiding full-table reloads. This incremental pattern minimises data movement and pipeline duration compared with repeatedly copying the entire source table.
- ✗
Use a tumbling window trigger with a data flow that processes all data each time.
Why it's wrong here
A tumbling window trigger schedules recurring runs but does not itself restrict rows; a data flow reading all data each time reloads the full table. Incremental loading requires a watermark or change-tracking query to select only new rows. Tumbling windows are correct for fixed-period batch scheduling, not row-level delta extraction.
- ✗
Use a mapping data flow with a full load and then use Azure Databricks to deduplicate.
Why it's wrong here
A full load plus Databricks deduplication reprocesses every row each run, so it never tracks changed rows incrementally. ADF's watermark column or change tracking pattern reads only new or modified records. The full-load approach suits one-off migrations or small tables where reprocessing cost is acceptable.
- ✗
Copy the entire table every time and use Azure Synapse serverless SQL to filter duplicates.
Why it's wrong here
Copying the whole table every run transfers all rows, and serverless SQL filtering afterwards cannot recover the wasted ingestion cost. Incremental loading uses a watermark column or change tracking to copy only changed rows. Full-copy-plus-filter suits scenarios where downstream deduplication is required regardless of source volume.
Go deeper
Related to this question
About these practice questions
This DP-203 question is part of Courseiva's 509-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.