DEA-C02 Data Transformation Practice Question
A data engineer is using Snowpark for Python to transform data. The engineer needs to perform a join between two DataFrames and then apply a filter based on a column from the joined result. Which TWO methods are appropriate for this transformation? (Choose two.)
⚠ Common exam trap
Test-takers frequently confuse Snowpark DataFrame methods with SQL statements or other DataFrame libraries, such as assuming merge() performs a join.
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 `DataFrame.join()` to combine the DataFrames, then `DataFrame.where()` to apply the condition.
In Snowpark, join() combines two DataFrames, and filter() or its alias where() applies a row-wise condition. These methods build a lazy query plan that executes on Snowflake. Other methods like merge(), union(), or select() do not perform a join followed by a row filter, so they are not appropriate for this transformation.
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 `DataFrame.union()` to combine the DataFrames, then `DataFrame.filter()` to apply the condition.
Why it's wrong here
union() concatenates rows from two DataFrames with the same schema, rather than joining them on a key. This produces a vertical stack, not a relational join. Applying a filter afterward would not achieve the intended join semantics and would likely yield incorrect results.
- ✓
Use `DataFrame.join()` to combine the DataFrames, then `DataFrame.where()` to apply the condition.
Why this is correct
DataFrame.where() is an alias for filter() in Snowpark. After joining with DataFrame.join(), calling where() applies the filter condition to the joined DataFrame. This is functionally equivalent to using filter() and is a valid method for applying conditions in a Snowpark transformation pipeline.
- ✓
Use `DataFrame.join()` to combine the DataFrames, then `DataFrame.filter()` to apply the condition.
Why this is correct
DataFrame.join() performs the join operation, returning a new DataFrame. Then DataFrame.filter() applies a row-wise condition on the joined result. This is a standard, lazy transformation approach in Snowpark that builds a query plan executed on Snowflake, making it efficient and appropriate for this scenario.
- ✗
Use `DataFrame.join()` to combine the DataFrames, then `DataFrame.select()` to apply the condition.
Why it's wrong here
select() is used to project columns or create expressions, not to filter rows. While you can include a conditional expression in select(), it does not remove rows; it returns a column with boolean or conditional values. To filter rows, you need filter() or where().
- ✗
Use `DataFrame.merge()` to combine the DataFrames, then `DataFrame.filter()` to apply the condition.
Why it's wrong here
Snowpark's DataFrame API does not have a merge() method. Merge is a SQL DML statement for updating or inserting rows, not a DataFrame join operation. Using merge() would cause an error, and it is not a valid way to combine two DataFrames for this transformation.
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.