DP-203 Develop data processing Practice Question
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?
⚠ Common exam trap
Many candidates 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.
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.
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
A Notebook activity with PySpark is incorrect because the transformation requires only row filtering and column derivation, which can be performed natively by a Data Flow activity without spinning up a Spark cluster; the stem specifies no need for custom Python or Scala libraries or complex machine-learning logic. It is tempting because PySpark notebooks are commonly used for large-scale data transformations in Azure Synapse, and would be the correct choice if the transformation involved advanced custom code, iterative algorithms, or integration with external libraries not available in Data Flows.
- ✗
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.
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