SnowPro Specialty: Snowpark (SPS-C01) — Practice Questions and Mock Exam

The SnowPro Specialty: Snowpark Certification (SPS-C01) validates expertise in using the Snowpark API for data engineering and data science on Snowflake, including DataFrame transformations, session management, stored procedures, user-defined functions, and client-side versus server-side result processing. It tests the ability to write production-grade Snowpark code that executes natively within Snowflake's compute environment.

This specialty certification is for data engineers and data scientists with one or more years of hands-on Snowpark experience in a production environment. Advanced Python and PySpark proficiency helps, as does familiarity with Snowflake's client-side and server-side execution model.

Data Transformations carry the most weight at 35%, testing DataFrame filtering, joins, aggregations, window functions, semi-structured data handling, and persisting results back to Snowflake. The Snowpark API for Python follows at 30%, covering session creation, DataFrame construction, and operationalizing UDFs, UDTFs, and stored procedures. Performance Optimization sits at 20%, testing Snowpark-optimized warehouses, caching, vectorization, and troubleshooting.

Snowpark Concepts is the lightest domain at 15%, covering architecture and setup rather than hands-on coding. Transformations and the API together make up nearly two-thirds of the exam, so fluency with the DataFrame methods themselves matters more than knowing Snowpark's underlying lazy-evaluation model in the abstract.

The exam consists of 55 questions in 85 minutes, drawn from multiple-choice, multiple-select, and interactive formats. Most items give you a transformation goal and ask you to pick the Snowpark method that achieves it: a DataFrame operation, a UDF pattern, or a session call. At roughly 1.5 minutes per question, fluency with filter, select, join, group_by, and agg keeps the pace comfortable.

Every domain of the SF-SP exam has enough questions in the pool to practice it in depth. A mock exam asks 55 questions in one sitting, on the same 85-minute clock as the real thing.

How long you'll need depends on how much hands-on experience you bring. The rest is set by the vendor: how the exam is delivered, how soon you can retake it, and how long the credential stays valid.

Delivered by online proctoring or at an onsite testing center, in English. The certification expires two years after your issue date; you recertify through the Snowflake Continuing Education program with an eligible instructor-led training course or an equivalent or higher-level SnowPro certification.

Developers who know PySpark but haven't written production Snowpark code often assume the APIs map directly, missing Snowpark-specific methods like merge, copy_into, and write_pandas that have no PySpark equivalent. The same gap shows up in execution-model questions: transformations like filter and select are lazy, while collect, show, and count trigger execution, and Snowpark stored procedures run server-side inside Snowflake under a caller- or owner-rights model rather than on a client machine. Scalar UDFs, vectorized UDFs, and UDTFs solve different problems, and the exam expects you to pick the right one for a given data shape.