- A
Use a 'Column pattern' to detect new columns and filter rows.
Why wrong: Column patterns are for handling schema drift, not row-level timestamps.
- B
Configure 'Row sampling' settings to take only new rows.
Why wrong: Row sampling takes a random percentage, not based on timestamp.
- C
Set 'Source' to 'New rows only' in the sink transformation.
Why wrong: Sink transformations do not have an option to filter new rows at source.
- D
Enable 'Incremental extraction' and specify the timestamp column for filtering.
Incremental extraction filters rows based on a watermark column like LastModified.
Question 310 of 851
DP-203 Develop data processing Practice Question
This DP-203 practice question tests your understanding of develop data processing. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 building a data pipeline in Azure Synapse Analytics that ingests JSON files from Azure Data Lake Storage Gen2, transforms the data using a mapping data flow, and loads it into a dedicated SQL pool. The pipeline must support incremental loads based on a LastModified timestamp in the source files. Which configuration should you use in the mapping data flow source transformation?
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
✓
Enable 'Incremental extraction' and specify the timestamp column for filtering.
Option D is correct because mapping data flows in Azure Synapse Analytics support incremental extraction by enabling the 'Incremental extraction' option in the source transformation and specifying a timestamp column (e.g., LastModified) to filter only new or updated rows. This allows the pipeline to load only changed data since the last run, which is essential for efficient incremental loads into a dedicated SQL pool.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 'Column pattern' to detect new columns and filter rows.
Why it's wrong here
Column patterns are for handling schema drift, not row-level timestamps.
- ✗
Configure 'Row sampling' settings to take only new rows.
Why it's wrong here
Row sampling takes a random percentage, not based on timestamp.
- ✗
Set 'Source' to 'New rows only' in the sink transformation.
Why it's wrong here
Sink transformations do not have an option to filter new rows at source.
- ✓
Enable 'Incremental extraction' and specify the timestamp column for filtering.
Why this is correct
Incremental extraction filters rows based on a watermark column like LastModified.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'incremental extraction' in the source transformation with sink-level settings or sampling options, mistakenly thinking that row sampling or sink properties can achieve incremental loads, when only the source's incremental extraction with a timestamp column provides the correct row-level filtering.
Detailed technical explanation
How to think about this question
Under the hood, incremental extraction in mapping data flows uses a watermark column (e.g., LastModified) and stores the last processed timestamp in pipeline metadata or an external control table. On each run, the source query dynamically filters rows where the timestamp is greater than the last watermark, ensuring only new or updated files are processed. This approach avoids reprocessing entire datasets and reduces latency, but requires that source files have a consistent timestamp column and that the pipeline handles watermark persistence correctly across runs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-203 question test?
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable 'Incremental extraction' and specify the timestamp column for filtering. — Option D is correct because mapping data flows in Azure Synapse Analytics support incremental extraction by enabling the 'Incremental extraction' option in the source transformation and specifying a timestamp column (e.g., LastModified) to filter only new or updated rows. This allows the pipeline to load only changed data since the last run, which is essential for efficient incremental loads into a dedicated SQL pool.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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