DEA-C02 Data Transformation Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Snowpark automatically caches all intermediate transformation results in memory on the client side to speed up iterative development.

  Why it's wrong here

  Snowpark does not automatically cache intermediate results on the client side. Data remains in Snowflake, and caching is managed by the Snowflake service, not the client. Client-side caching would be impractical for large datasets and is not a feature of Snowpark. This statement misrepresents how Snowpark handles data.

• ✗

  Snowpark requires data to be extracted to a local Python environment for any transformation that uses third-party libraries.

  Why it's wrong here

  Snowpark is designed to push computations to Snowflake, and it supports third-party libraries within UDFs without extracting data. You can package dependencies and run them in Snowflake. Extracting data locally is not required and would be inefficient. This statement is incorrect because Snowpark avoids data movement.

• ✓

  Snowpark allows you to write transformations using a DataFrame API that is lazily evaluated and executed on the Snowflake compute engine.

  Why this is correct

  Snowpark's DataFrame API is designed for lazy evaluation, meaning transformations are not executed until an action is triggered. The operations are pushed down to Snowflake's engine for execution, leveraging its scalability. This is a core feature that enables efficient processing of large datasets without pulling data to the client.

• ✗

  Snowpark provides a built-in visual interface for designing transformation pipelines without writing code.

  Why it's wrong here

  Snowpark is a code-first API available in Python, Java, and Scala. It does not provide a built-in visual interface for designing pipelines. While Snowflake offers other tools like Snowsight for SQL, Snowpark itself is programmatic. This statement is false and misrepresents Snowpark's capabilities.

• ✓

  Snowpark supports user-defined functions (UDFs) written in Python, Java, and Scala, which can be used within transformations.

  Why this is correct

  Snowpark allows developers to create UDFs in multiple languages, including Python, Java, and Scala. These UDFs can be invoked within DataFrame transformations or SQL, enabling custom logic. This flexibility is a key feature for complex transformations that require language-specific libraries or algorithms.