DP-203 Develop data processing Practice Question
Which THREE options are valid ways to transform data in Azure Synapse Analytics?
⚠ Common exam trap
Many exam-takers confuse Power Query Online (a Power BI/ADF feature) with Mapping Data Flows (a Synapse pipeline activity), or assume Azure Machine Learning pipelines are valid for data wrangling in Synapse, when in fact they are separate services for ML lifecycle management.
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 T-SQL scripts in a dedicated SQL pool.
T-SQL scripts are a native and primary method for transforming data within a dedicated SQL pool in Azure Synapse Analytics. You can use CREATE TABLE AS SELECT (CTAS), INSERT...SELECT, and other T-SQL statements to perform complex transformations like aggregations, joins, and data cleansing directly on the distributed data, leveraging the MPP (Massively Parallel Processing) engine for high performance.
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 Power Query online in Synapse pipelines.
Why it's wrong here
Power Query is not integrated into Synapse pipelines; it is part of Power BI.
- ✓
Use T-SQL scripts in a dedicated SQL pool.
Why this is correct
T-SQL is a primary way to transform data in Synapse SQL pools.
- ✓
Use Mapping Data Flows in Synapse pipelines.
Why this is correct
Data Flows provide visual transformations.
- ✓
Use Spark notebooks in Synapse Spark pools.
Why this is correct
Spark notebooks allow code-based transformations.
- ✗
Use Azure Machine Learning pipelines for data wrangling.
Why it's wrong here
Azure ML pipelines are for ML workflows, not general data transformation.
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