Snowflake · SF-SP · Intermediate
SnowPro Specialty: Snowpark (SPS-C01) — Practice Questions and Mock Exam
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Checked against Snowflake · April 2026Current exam version
Overview
About the 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 at least one year of hands-on Snowpark experience, advanced Python and PySpark proficiency, and familiarity with Snowflake's execution model. It validates a critical skill set as organizations shift from SQL-only to multi-language data processing on Snowflake.
Exam Domains
What's on the exam
The exam consists of 60 questions — multiple-choice and multiple-select — to be completed in 85 minutes. Questions cover domains including DataFrame Operations & Transformations, Session Management & Connectivity, Stored Procedures & UDFs, Action vs Transformation Laziness, and Client-Side vs Server-Side Processing. A passing score is 750 out of 1000. DataFrame API questions dominate — ensure fluency with filter, select, join, groupBy, and agg operations.
Understand Snowpark architecture, DataFrame API, and session management.
Use Snowpark Python API for data manipulation, UDFs, stored procedures, and ML integration.
Build data pipelines and transformations using Snowpark DataFrames, joins, aggregations, and window functions.
Optimize Snowpark queries, caching, pushdown operations, and warehouse sizing.
SourceSnowflake exam page
Format
What to expect
Watch out
Where candidates struggle
Developers who know PySpark but haven't used Snowpark specifically often trip on differences in lazy evaluation behavior, session object patterns, and Snowpark-specific DataFrame methods.
- 01Lazy vs Eager Evaluation — Not understanding which Snowpark operations are lazy (transformations like filter, select) versus eager (actions like collect, show, count) leads to execution order question errors.
- 02Session Object Scope — Misunderstanding Snowpark session lifecycle — how sessions are created, how they map to Snowflake connections, and their thread-safety constraints — causes connectivity question mistakes.
- 03UDF Types — Confusing scalar UDFs, vectorized UDFs (with pandas), and UDTFs (table functions) and not knowing when each is appropriate leads to function design question failures.
- 04Snowpark vs PySpark — Assuming PySpark APIs translate directly to Snowpark and missing Snowpark-specific methods (e.g., merge, copy_into, write_pandas) causes API knowledge question errors.
- 05Stored Procedure Execution — Not understanding that Snowpark stored procedures run inside Snowflake (server-side) with a caller or owner rights model leads to security and execution context mistakes.
Details
Exam logistics
Delivered online via the Snowflake Certification Portal. Available in English. The certification is valid for 2 years. Renewal requires recertification or continuing education credits. Exam fee is $250 USD. Prerequisite: active SnowPro Core certification.
SourceSnowflake exam page
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