SnowPro Advanced: Data Scientist (DSA-C01) Practice Test

Snowflake recommends 2+ years of practical experience with data science and at least 6 months to 1 year of hands-on work with Snowflake, specifically using features like Snowpark for Python/Scala, UDFs, and stored procedures. Conceptual knowledge alone is insufficient.

While you need a strong understanding of ML concepts, algorithms, and lifecycle, the exam focuses more on how to operationalize these within Snowflake. You should know how to integrate popular open-source libraries via Snowpark, but in-depth framework specialization is less critical than understanding the deployment and management patterns on the platform.

Both are essential. You must be proficient in advanced SQL for data preparation and transformation within Snowflake. Simultaneously, you need strong Python (or Scala) skills for developing UDFs, using Snowpark DataFrames, and building model logic. The exam tests your ability to choose the right tool (SQL or programmatic) for different stages of the workflow.

It covers them from a data scientist's perspective. You need to understand concepts like virtual warehouses (for compute scaling), data sharing, security (roles & privileges), and cost management as they relate to building and running efficient, governed data science workloads. You are not expected to be a platform admin, but you must know how to architect solutions within platform constraints.

The exam format is multiple-choice and multiple-select. While there are no live, interactive lab environments, many questions are scenario-based and require you to apply knowledge to choose the correct code snippet, architecture diagram, or series of steps to solve a practical problem.