- A
Stored procedure activity
Why wrong: Stored procedures are for SQL databases, not file transformations.
- B
Notebook activity with PySpark
Why wrong: While possible, data flow is more appropriate for no-code transformation.
- C
Copy data activity
Why wrong: Copy activity does not support row-level transformations.
- D
Data flow activity
Data flows provide visual transformation with built-in mapping.
Question 656 of 851
DP-203 Develop data processing Practice Question
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.
Your company uses Azure Data Lake Storage Gen2 as a data lake. You need to process CSV files that arrive in a 'raw' container, transform them into Parquet format, and write them to a 'curated' container. The transformation includes filtering out rows with null values in the 'customer_id' column and adding a partition column 'year' based on the 'order_date'. You use Azure Synapse Pipelines. Which activity should you use for the 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
✓
Data flow activity
The Data Flow activity in Azure Synapse Pipelines is designed for code-free, visual data transformations at scale. It can directly read CSV files from the 'raw' container, filter out rows with null 'customer_id' values using a conditional split or filter transformation, derive a 'year' column from 'order_date' using a derived column transformation, and write the results as Parquet files to the 'curated' container—all without writing code. This makes it the optimal choice for this ETL scenario.
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.
- ✗
Stored procedure activity
Why it's wrong here
Stored procedures are for SQL databases, not file transformations.
- ✗
Notebook activity with PySpark
Why it's wrong here
While possible, data flow is more appropriate for no-code transformation.
- ✗
Copy data activity
Why it's wrong here
Copy activity does not support row-level transformations.
- ✓
Data flow activity
Why this is correct
Data flows provide visual transformation with built-in mapping.
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 often confuse the Copy data activity's basic mapping capabilities with the full transformation logic needed for filtering and deriving new columns, leading them to choose Option C instead of recognizing that Data Flow is required for row-level and column-level transformations.
Detailed technical explanation
How to think about this question
Under the hood, Data Flow activities in Synapse execute on Apache Spark clusters managed by the integration runtime, translating visual transformations into optimized Spark jobs. The derived column transformation uses expression functions like `year(order_date)` to compute the partition column, and the sink automatically partitions output Parquet files by that column when 'partition by' is configured. A real-world scenario where this matters is when you need to enforce schema drift handling—Data Flow can dynamically adapt to new columns in incoming CSV files without pipeline failures.
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: Data flow activity — The Data Flow activity in Azure Synapse Pipelines is designed for code-free, visual data transformations at scale. It can directly read CSV files from the 'raw' container, filter out rows with null 'customer_id' values using a conditional split or filter transformation, derive a 'year' column from 'order_date' using a derived column transformation, and write the results as Parquet files to the 'curated' container—all without writing code. This makes it the optimal choice for this ETL scenario.
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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