Home Certifications FAQ Partners Blog

·EN·DE·FR·ES·PT

Snowflake · SF-SP · Intermediate

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

Prepare for SF-SP with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

Start practicing SF-SP free Buy SF-SP access for $29.99

Buy SF-SP access for $29.99 Start practicing SF-SP free

55Mock exam questions

85minTime limit

750/ 1000Passing score

Checked against Snowflake · August 2026 · Current 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 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.

Try the practice preview

Try five SF-SP questions

Try five practice questions from the app’s current SnowPro Specialty: Snowpark (SPS-C01) question bank, with answers and explanations.

Snowpark Performance Optimization1 / 5

What is the purpose of the query_tag session parameter in Snowpark?

AlexFull explanation from Alex

The query_tag session parameter sets an arbitrary string attached to every SQL query executed within the session. This tag appears in QUERY_HISTORY views and Snowsight, enabling teams to identify, group, and monitor queries by application, module, or pipeline. Set via session.query_tag = 'my_tag'. Why others are wrong: query_tag has no effect on query execution priority or scheduling order. It does not limit concurrency—concurrency is controlled by warehouse settings. It does not enable or affect result caching; caching is managed independently by Snowflake's query result cache. Ref: docs.snowflake.com/en/developer-guide/snowpark/reference/python/api/snowflake.snowpark.Session.query_tag

Sourcedocs.snowflake.com

Snowpark for Data Transformations2 / 5

In a Snowpark ML Pipeline containing both a StandardScaler and an XGBClassifier, what happens when pipeline.fit(train_df) is called?

AlexFull explanation from Alex

When pipeline.fit(train_df) is called, steps execute sequentially: the StandardScaler is fitted on the training data and transforms it, then the XGBClassifier trains on the scaler's transformed output. Each intermediate transformer must implement fit() and transform(). Why others are wrong: The pipeline fits all steps automatically—no separate train() call is needed. Steps are not compiled into a single SQL query; each step executes independently in sequence. No dynamic table is created; pipeline processing uses DataFrame transformations, not materialized views. Ref: docs.snowflake.com/en/developer-guide/snowpark-ml/reference/latest/api/modeling/snowflake.ml.modeling.pipeline.Pipeline

Sourcedocs.snowflake.com

Snowpark API for Python3 / 5

Which method saves a DataFrame as a permanent or temporary Snowflake table?

AlexFull explanation from Alex

df.write.save_as_table("my_table") saves a DataFrame as a permanent or temporary Snowflake table via the DataFrameWriter. It supports mode (append, overwrite, truncate, errorifexists, ignore), table_type (temp, transient, permanent), and clustering_keys parameters. Why others are wrong: The option “df.to_table("my_table")” to_table() is not a valid Snowpark DataFrame method. persist() does not exist on DataFrame or DataFrameWriter. export() is not part of the Snowpark API. Ref: docs.snowflake.com/en/developer-guide/snowpark/reference/python/api/snowflake.snowpark.DataFrameWriter.save_as_table

Sourcedocs.snowflake.com

Snowpark Concepts4 / 5

How do you create a Snowpark DataFrame from an existing Snowflake table?

AlexFull explanation from Alex

session.table("table_name") is the standard Snowpark API for creating a lazily-evaluated DataFrame referencing an existing Snowflake table. It returns a Table object (a DataFrame subclass) without immediately loading data. Name can be fully qualified: 'db.schema.table'. session.read.table() is not a valid Snowpark method — Snowpark does not use a .read accessor like PySpark. DataFrame.load() does not exist in the Snowpark API. session.query() is not a method; use session.sql() for raw SQL, but session.table() is preferred for direct table references. Ref: docs.snowflake.com/en/developer-guide/snowpark/reference/python/latest/snowpark/api/snowflake.snowpark.Session.table

Sourcedocs.snowflake.com

Snowpark for Data Transformations5 / 5

Which Snowpark function converts a string column to uppercase?

AlexFull explanation from Alex

The upper() function from snowflake.snowpark.functions converts a string column to uppercase, translating to the SQL UPPER() function. Supports Unicode. Usage: functions.upper(col('name')). Why others are wrong: col('name').upper() is not a valid Column method—upper is a standalone function, not a column method. The option “functions.to_upper(col('name'))” to_upper() does not exist in snowflake.snowpark.functions; the correct name is upper(). .str.upper() is a pandas accessor pattern, not available on Snowpark Column objects. Ref: docs.snowflake.com/en/developer-guide/snowpark/reference/python/api/snowflake.snowpark.functions.upper

Sourcedocs.snowflake.com

Create a free account

Question pool

360 practice questions

The Pass-IT question pool gives you material to practice for SF-SP. A Pass-IT mock exam uses 55 questions and a 85-minute time limit; these are practice settings.

Pool details: SF-SP

Exam details checked against SnowflakeAugust 14, 2026

date of the last check against the official Snowflake source

Passing score750 / 1,000

as published by Snowflake

Objectives in the guide16 objectives listed in the official guide

across 4 domains in the official exam guide

Pool size360 questions

= The pool size is equivalent to 6 sets of 55 questions; this does not mean that each mock exam uses a separate set.

Blueprint domains4 domains in the exam blueprint

Snowpark Concepts 55 · Snowpark API for Python 106 · Snowpark for Data Transformations 126 · Snowpark Performance Optimization 73

Recorded as checked against sources360 of 360

questions recorded as having their answer, options, and explanation checked against official Snowflake documentation

How questions are made →

Exam domains

What's on the exam

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.

Exam blueprint: SF-SP

Snowpark Concepts15%

Understand Snowpark architecture, DataFrame API, and session management.

≈ 8 h

Snowpark API for Python30%

Use Snowpark Python API for data manipulation, UDFs, stored procedures, and ML integration.

≈ 15 h

Snowpark for Data Transformations35%

Build data pipelines and transformations using Snowpark DataFrames, joins, aggregations, and window functions.

≈ 18 h

Snowpark Performance Optimization20%

Optimize Snowpark queries, caching, pushdown operations, and warehouse sizing.

≈ 10 h

Format

Exam format and question types

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.

Question types: SF-SP

Multiple Choice70%

Select the single answer that best meets the question’s requirements.

Multiple Response30%

Select multiple answers. Follow the question’s instructions on how many to choose.

See Snowflake for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for SF-SP

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.

Preparation and logistics: SF-SP

Preparation

Illustrative study time30–75 h

illustrative planning range: 30 h with relevant experience to 75 h when starting out; your needs may fall outside this range

LevelIntermediate

Recommended backgroundNo prerequisites. 1 or more years of hands-on Snowpark experience in a production environment recommended. Advanced Python and PySpark proficiency is helpful.

Taking and maintaining the certification

DeliveryOnline proctored or onsite testing centers.

Retake policyLimit of 4 attempts in a 12-month period. After three attempts Snowflake recommends attending an onsite Snowflake training course. Each registration requires the full registration fee.

Certification validity2 years

Snowflake certifications expire two years after the certification issue date. Recertify through the Snowflake Continuing Education (CE) program: complete an eligible Snowflake Instructor-Led (ILT) training course, or earn an equivalent or higher-level SnowPro certification. A valid certification is required to take part in the CE program.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: SF-SP

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.

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

How long is the SnowPro Specialty: Snowpark (SPS-C01) exam?

The SnowPro Specialty: Snowpark (SPS-C01) exam has 55 questions and a 85-minute time limit.

What is the passing score for SnowPro Specialty: Snowpark (SPS-C01)?

The passing score for the SnowPro Specialty: Snowpark (SPS-C01) exam is 750 / 1000.

Which pitfalls should I review when preparing for SnowPro Specialty: Snowpark (SPS-C01)?

Topics to review include Lazy vs Eager Evaluation, Session Object Scope, UDF Types, Snowpark vs PySpark, Stored Procedure Execution. Work through examples to check that you understand the distinctions and can explain your answer.

What does the Snowpark specialty exam cover?

Snowpark for data transformations is the largest section at 35%, with the Snowpark API for Python at 30% and performance optimization at 20%. Snowpark concepts take the remaining 15%. Two thirds of the exam is therefore writing transformations and using the Python API rather than describing what Snowpark is.

Do you need SnowPro Core before the Snowpark exam?

No. Snowpark is a specialty exam and carries no certification prerequisite, unlike the advanced range. Snowflake recommends a year or more of hands-on Snowpark work in production and notes that advanced Python and PySpark fluency helps. The catalog budget is around 50 hours.

How many times can you take the Snowpark exam?

Four times in any 12-month period. After three attempts Snowflake recommends an onsite training course instead of another booking.

What pairs well with the Snowpark certification?

SnowPro Core is the obvious companion, since it is the platform exam every advanced certification requires and Snowpark does not. The Advanced Data Engineer exam is the step up if transformations are your whole job, with 28% on data movement and 25% on transformation. Native Apps is the other specialty exam if you are packaging what you build.

One certification, 12 months

Practice for SF-SP

Focus your practice on one certification, or choose Pro to practice across all certifications.

Buy SF-SP access for $29.99

Start a free practice sessionTry the first 20 questions without a card to see whether the practice suits you.

For eligible purchases: money-back guarantee if you fail your exam.

View guarantee terms →