DEA-C02 Data Transformation Practice Question
A data engineer is building a transformation pipeline that must read semi-structured events from a stage, parse them, and write the output to a target table. The pipeline will run on a schedule and must be version-controlled and testable like other software artifacts. The team wants to avoid writing SQL scripts that are hard to unit test. Which Snowflake feature should the engineer use to implement this transformation?
⚠ Common exam trap
The trap here is assuming that any scheduled SQL execution provides the same software engineering benefits as a programming language, when in fact testability and version control are not inherent to SQL scripts.
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
✓
Snowpark with a Python stored procedure
Snowpark is the appropriate choice because it allows developers to write transformation logic in Python, which can be unit tested and version controlled. It also integrates with Snowflake's compute and handles semi-structured data. The other options either lack testability, are not designed for complex transformations, or are technically invalid for the described use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A materialized view over the stage
Why it's wrong here
Materialized views cannot be created directly over a stage; they are defined on tables and have restrictions on the types of queries they support. They also do not provide a programming environment for testable transformations. This option misrepresents both the capability of materialized views and the requirements of the scenario.
- ✗
A series of SQL scripts executed by a task
Why it's wrong here
SQL scripts are difficult to unit test in isolation and often lack the structure needed for version-controlled software practices. While tasks can schedule SQL, they do not provide the testing and modularity benefits of a programming language. The scenario specifically calls for testable, version-controlled code, which SQL scripts alone do not satisfy as cleanly.
- ✗
Snowpipe with a transformation function
Why it's wrong here
Snowpipe is designed for continuous data loading, not for complex transformations that require testing and version control. It can call a function during load, but that function is still SQL-based and not easily unit tested. The scenario asks for a transformation pipeline that is testable and version-controlled, which is not Snowpipe's primary strength.
- ✓
Snowpark with a Python stored procedure
Why this is correct
Snowpark lets the engineer write transformations in Python, which can be unit tested with standard frameworks before deployment. The code can be stored in a Git repository and executed as a stored procedure or in a Snowpark session. This aligns with the requirement for version control and testability, unlike pure SQL scripts. It also handles semi-structured data natively through DataFrame APIs.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Snowflake exam blueprint
This DEA-C02 practice question is part of Courseiva's free Snowflake certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C02 exam.