DP-203 Develop data processing Practice Question
You need to transform data in Azure Synapse Analytics using a language that supports procedural logic and error handling. Which option should you use?
⚠ Common exam trap
It's easy for candidates to confuse PolyBase's ability to query external data with the ability to perform procedural transformations, overlooking that PolyBase is a query engine, not a programming construct for logic and error handling.
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
✓
T-SQL stored procedures
T-SQL stored procedures are the correct choice because they support procedural logic (e.g., IF/ELSE, loops, TRY/CATCH) and error handling within Azure Synapse Analytics dedicated SQL pools. This allows you to encapsulate complex data transformation logic, handle runtime errors gracefully, and manage transactions, which is not possible with declarative objects like views or external tables.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
T-SQL stored procedures
Why this is correct
T-SQL stored procedures provide procedural constructs such as IF, WHILE, TRY/CATCH and transactions within Synapse, satisfying the requirement for procedural logic and error handling. Spark notebooks and Mapping Data Flows offer transformation but not native T-SQL error-handling semantics against dedicated SQL pools.
- ✗
CREATE VIEW
Why it's wrong here
A view stores a SELECT statement; it cannot hold IF/ELSE branching, TRY/CATCH or loops, so procedural logic is impossible. Views are tempting for encapsulating reusable read-only queries, and would be the right choice when the requirement is simply to expose a saved, reusable result set rather than execute control flow.
- ✗
PolyBase
Why it's wrong here
PolyBase is a connectivity layer that queries external data through T-SQL external tables; it provides no procedural constructs such as variables, loops or TRY/CATCH. It is tempting because it does transform and move data, and would be correct when the task is querying or ingesting files from Azure Data Lake or Blob Storage.
- ✗
CREATE EXTERNAL TABLE
Why it's wrong here
CREATE EXTERNAL TABLE only defines metadata pointing at files in a data source; it executes no transformation logic and offers no branching or error handling. It is tempting because external tables are central to Synapse data movement, and would be correct when the requirement is to query Parquet or CSV data in place.
Go deeper
Related to this question
Learn chapter
Orchestrate Data Movement and Transformation
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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