SnowPro Specialty: Snowpark Certification
Develop, test, and optimize complex data engineering, machine learning, and application development workflows using Snowpark for Python, Java, and Scala in Snowflake.
Official Exam Specification & Quick Facts
Question Difficulty Breakdown
Exam Guidelines & Simulation Rules
Frequently Asked Questions (FAQ)
The exam consists of 65 scenario and code-based questions (Python, Java, Scala) over 115 minutes. Cost is $375 USD with a passing mark of 750/1000 (75%). SnowPro Core is a required prerequisite.
Domains include: (1) Snowpark Architecture and Execution Model (~25%), (2) DataFrame API Operations (~30%), (3) Stored Procedures and User-Defined Functions (~25%), and (4) Performance Tuning and Packaging (~20%).
Snowpark DataFrames are lazily evaluated. DataFrame transformations construct an abstract syntax tree (AST) that is only compiled into optimized SQL and executed in virtual warehouses when an action (e.g. `collect()`, `show()`) is called.
Pandas executes in-memory on a single machine and is constrained by local RAM. Snowpark Python compiles DataFrame operations into SQL executed in parallel across Snowflake virtual warehouses on petabytes of data.
Vectorized UDFs accept and return pandas Series or DataFrames, processing batches of rows simultaneously using vectorized NumPy/C optimizations rather than row-by-row iteration.
Snowflake provides built-in Anaconda channel integration, allowing developers to import thousands of vetted third-party Python packages (`snowflake.snowpark.Session.add_packages()`) without local package management.
UDTFs are functions that process input parameters and return an entire tabular result set (multiple rows and columns), useful for custom windowing, parsing, and data generation.
An anonymous UDF is defined inline in code for the lifetime of a specific Snowpark session. A permanent UDF is registered with a name in a database schema and can be called by other users and SQL queries.
External Python modules, custom wheel packages, and JAR files can be uploaded to an internal Snowflake stage and referenced in the Snowpark session using `session.add_import()`.
Developers inspect the compiled SQL generated by Snowpark DataFrame operations using the `df.queries` property or reviewing `QUERY_HISTORY` in the Snowflake console.
The credential is valid for two years from the pass date.
This specialty certifies Senior Snowpark Developers, Python Data Platform Engineers, and Cloud ML Engineers earning average US salaries between $140,000 and $185,000.