EnglishDeutschFrançaisEspañolPortuguês

Snowflake · SF-SP · Intermediate

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.

Practice for freeTry a practice question first
Try a practice question firstPractice for free
Start practicing SF-SP freeBuy SF-SP access for $29.99
55Questions per Pass-IT mock exam
85minPass-IT mock exam time limit

Official SF-SP exam: 750 out of 1000 points to pass.

Checked against Snowflake · September 2026 · Current exam version

Try Pass-IT

See what learning with Pass-IT is like.

Answer a SF-SP sample question and read the explanation with its source. Right here, with no sign-up.

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 Concepts3 / 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 Transformations4 / 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

Snowpark API for Python5 / 5

Which import provides the PandasSeries type hint for vectorized UDFs?

AlexFull explanation from Alex

PandasSeries is defined in snowflake.snowpark.types alongside other Snowpark type definitions like StringType, IntegerType, FloatType, etc. Import: from snowflake.snowpark.types import PandasSeries. Why others are wrong: pandas.Series from the pandas library is a different class—PandasSeries is a Snowpark-specific type hint for vectorized UDF signatures. snowflake.snowpark.functions contains SQL function wrappers (upper, sum, col), not type definitions. snowflake.snowpark.udf contains UDF registration utilities, not type hint classes. Ref: docs.snowflake.com/en/developer-guide/snowpark/reference/python/api/snowflake.snowpark.types

Sourcedocs.snowflake.com

How to prepare for SF-SP with Pass-IT.

Currently 360 practice questions for SF-SP, with new ones added regularly. Focus on what you still need to learn.

Get past difficult questions.

Understand why an answer is correct. Alex explains the concepts and answers your follow-up questions so you can apply what you learn to the next task.

Meet Alex →

Conversation preview · SF-SP

Query tags

  1. You

    How can I identify queries from my Snowpark application in monitoring?

  2. Alex · AI tutor

    Set a query_tag for the session.

    Read the Snowflake documentation ↗
  3. You

    Does the tag change the query result?

  4. Alex · AI tutor

    Its purpose here is identification and monitoring, so you can recognize the tagged queries.

    Read the Snowflake documentation ↗

Make your study time count.

Practice at your level.
For new practice sessions, Pass-IT adjusts the difficulty based on your previous answers.

Learn from mistakes.
Incorrect answers automatically become flashcards. When you review them, you rate how confidently you remember; Pass-IT schedules the next review.

See how focused practice works →

Animated feature preview

Turn your mistakes into knowledge you can rely on.

How Pass-IT works

Your review

Answered a question incorrectly?

Pass-IT saves it for your review.

SF-SP · Question excerpt

Query tags

View the full question

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

Understand the correct answer.

It adds a tag string to queries for identification and monitoring purposes

query_tag attaches an identifying tag to queries for monitoring.

Read the Snowflake documentation ↗

How confidently do you remember?

During review, you assess how well you still know the answer.

Correct after hesitating

Pass-IT schedules your next review.

The date depends on your self-assessment and previous reviews.

Simplified demonstration. No learning progress is saved.

Practice for the moment that counts.

Take a full mock exam with a time limit and find out whether you can finish in time.

Your SF-SP mock exam

Questions
55
Minutes
85

Illustration of an exam session

  1. Start
  2. Answer questions
  3. Finish

Length and time limit of the Pass-IT mock exam.

Go into your exam with more confidence.

Your Readiness Score helps you assess whether you are ready for the exam. Results by exam domain show you where you should keep practicing.

Your money back if you fail despite 80% Readiness.
If your Readiness Score is at least 80% when you take the exam and you still fail, we refund the Pass-IT purchase price for this certification.

For active, paid access. Submit proof of failing within 14 days.

Further guarantee conditions

You need at least 80% Readiness and active, paid access to the certification when you take the exam. Take the exam within your access period and submit official proof of failing within 14 days after the exam.

For purchases through Apple or Google, refunds are handled by the respective store. See the full terms for further details.

Read the guarantee terms →

The Readiness Score describes your practice progress, not a probability of passing.

Guarantee requirement

80%

At least 80% Readiness
when you take the exam

Money back if you fail

Question counts and mock exam settings from the current Pass-IT catalog.

Exam domains

What you practice for SF-SP.

Exam topics, relevant practice and sources you can check.

Understand and set up SnowparkSee the guide for weighting55 QuestionsPass-IT questions

Connect Snowpark’s execution model to your development environment.

  • Explain lazy evaluation and client-side or server-side execution with DataFrames, UDFs, UDTFs, procedures and file operations; distinguish DataFrame and ML libraries and package sources.
  • Install a compatible Python and Snowpark environment and work in Snowflake Notebooks, Jupyter, VS Code or other development tools.
Official Snowflake objectives
Use the Snowpark Python APISee the guide for weighting106 QuestionsPass-IT questions

Create sessions and reusable code that operate on Snowflake data.

  • Create authenticated sessions with account identifiers, connection parameters and SessionBuilder; use session methods, attributes and asynchronous jobs.
  • Read unstructured files with SnowflakeFile and process them through UDFs, UDTFs or stored procedures.
  • Create DataFrames from tables, views, Python objects, SQL, files or pandas and apply suitable schemas and data types.
  • Write and register UDFs and UDTFs from code or files with their modules and type declarations; compare scalar and vectorized execution and grant secure access.
  • Build and register procedures with packaged dependencies and explicit types, choose caller or owner rights, and run reusable workflows or task graphs through the Python API.
Official Snowflake objectives
Transform data with DataFramesSee the guide for weighting126 QuestionsPass-IT questions

Build transformations and decide how their results should be used.

  • Filter, sort, limit and cast values with columns, scalar functions and operators, and extract values from Row objects.
  • Clean and enrich data with joins, missing-value handling and sampling, then apply aggregates, windows, grouping, set operations and table or user-defined functions.
  • Load and traverse semi-structured data, cast nested values and flatten arrays of objects into rows.
  • Persist DataFrame results as views, Snowflake tables or staged files.
  • Insert, update, delete and merge table data through Snowpark DataFrames.
Official Snowflake objectives
Tune and troubleshoot SnowparkSee the guide for weighting73 QuestionsPass-IT questions

Improve execution without losing sight of warehouse cost.

  • Choose Snowpark-optimized warehouses, configure their properties and scale them according to workload and billing effects.
  • Cache or materialize DataFrames, compare scalar and vectorized UDFs or pandas on Snowflake, and choose synchronous or asynchronous calls.
  • Investigate errors and bottlenecks with event tables, query history and equivalent SQL; test code with the Snowpark local testing framework and pytest.
Official Snowflake objectives
Question formats you can practice
Multiple Choice
Select one answer.
Multiple Response
Select multiple applicable answers.

These are Pass-IT practice formats. Check the Snowflake certification page for the official exam information.

Frequently asked questions

Your preparation

How much study time should I plan for SF-SP?

For planning, Pass-IT estimates roughly 30–75 hours. Relevant experience may put you toward the lower end; starting from scratch may put you toward the higher end. Your needs may fall outside this range. This is not a time requirement from Snowflake or a promise that you will be exam-ready within that time.

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.

Using Pass-IT

What language are Pass-IT questions in?

Questions in the app are currently in English. Translations of the sample questions on this page apply only to this preview. Check with Snowflake for the languages available for the official exam.

How long does my SF-SP access last?

Access covers this certification for 12 months. You pay once, with no automatic renewal.

When does the money-back guarantee apply?

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days. Guarantee terms

About the SF-SP exam

Where can I take the SF-SP exam?

Online proctored or onsite testing centers.

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.

How many times can you take the Snowpark exam?

You can make at most four attempts at the same exam in any 12-month period, with seven days between all attempts, including before your first retake. Snowflake retires the exam effective 1 November 2026. Every attempt, including a retake, must be completed by that date, so failing close to the deadline may leave you no time for a retake.

Your preparation for SF-SP

$29.99one-time

12 months of access for SF-SP · No automatic renewal

Buy SF-SP access

Questions in the app are currently in English.

Try 20 questions for freeFree account required. No credit card.

Already have an account? Log in

Prefer a subscription? View subscriptions →

Money back if you fail with at least 80% Readiness, for eligible purchases under the guarantee terms.

Guarantee terms

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days.

Full guarantee terms →

Related certifications

SnowPro Advanced: Administrator (ADA-C02)SnowPro Advanced: Architect (ARA-C01)SnowPro Advanced: Data Analyst (DAA-C01)SnowPro Advanced: Data Engineer (DEA-C02)All Snowflake certifications →