DEA-C02 Data Transformation Practice Question
A data engineer needs to perform a complex transformation that involves reading from a source table and writing to multiple target tables in a single transaction. What is the recommended strategy?
⚠ Common exam trap
Candidates often confuse individual SQL statement execution with atomic transactions. They forget that without an explicit BEGIN/COMMIT block, each statement executes independently, risking partial updates if a failure occurs.
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
✓
Wrap the multiple INSERT statements in a single BEGIN...COMMIT transaction block.
Using an explicit transaction block (BEGIN...COMMIT) ensures that multiple DML statements are treated as a single atomic unit. This is critical for data consistency, particularly when updating related tables simultaneously. If any part of the transformation fails, the transaction can be rolled back, ensuring the system remains in a valid, predictable state, which is a core requirement for reliable enterprise data pipelines.
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 an asynchronous task for each target table to run in parallel.
Why it's wrong here
Parallel tasks do not share transactional context by default, which can lead to data inconsistencies if one task succeeds and another fails. Transactions are required to ensure atomicity across multiple operations, which cannot be guaranteed by independent, asynchronous tasks.
- ✓
Wrap the multiple INSERT statements in a single BEGIN...COMMIT transaction block.
Why this is correct
Wrapping multiple DML operations in a BEGIN/COMMIT block guarantees atomicity. If any statement fails, the entire transaction can be rolled back, ensuring that either all target tables are updated correctly or none are, maintaining the integrity of the data across the whole schema.
- ✗
Create a separate stored procedure for each insert to reduce complexity.
Why it's wrong here
Splitting logic into multiple procedures makes it harder to manage transactional integrity across operations. It is better to encapsulate the entire transformation logic within a single procedure or block to ensure that all modifications occur as one atomic unit of work.
- ✗
Use a series of independent views to union the data instead of writing to tables.
Why it's wrong here
Views are for reading, not for data persistence. Using them to avoid multi-table writes ignores the requirement to actually transform and store data in target tables, which is the core objective of the data transformation pipeline being described.
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Last reviewed September 2026 · checked against the official Snowflake exam blueprint
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