Exam Name: Snowflake Certified SnowPro Specialty - Snowpark
Certification Provider: Snowflake
Corresponding Certification: Snowflake Certification
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Snowflake Certified SnowPro Specialty - Snowpark: SPS-C01 Exam
"Snowflake Certified SnowPro Specialty - Snowpark", also known as SPS-C01 exam, is a Snowflake Certification. With the complete collection of questions and answers, iPassleader has assembled to take you through 374 Q&As to your SPS-C01 Exam preparation. In the SPS-C01 exam resources, you will cover every field and category in Snowflake Certification Certification helping to ready you for your successful Snowflake Certification.
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- Updated on: Oct 25, 2026
- No. of Questions: 374 Questions & Answers
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- Updated on: Oct 25, 2026
- No. of Questions: 374 Questions & Answers
Short on study time for the Snowflake Certified SnowPro Specialty - Snowpark? iPassleader condenses the SPS-C01 exam into 374 carefully prepared practice questions, so every spare hour moves you closer to a pass. Start today and turn scattered minutes into real progress.
Snowflake SPS-C01 Exam Overview:
| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | Snowflake Certified SnowPro Specialty - Snowpark |
| Exam Number: | SPS-C01 |
| Passing Score: | 750 (scaled 0-1000) |
| Real Exam Qty: | 55 |
| Exam Format: | Multiple Choice, Interactive, Multiple Select |
| Exam Price: | $225 USD |
| Available Languages: | English |
| Related Certifications: | SnowPro Core Certification |
| Exam Duration: | 85 minutes |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online Proctored or Onsite Testing Center |
| Pre Condition: | SnowPro Core Certification is required. |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-snowpark |
Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark API for Python | 30% | - Establishing connections and session management - Working with Semi-structured data - Reading and writing data - DataFrame creation and manipulation - User-Defined Functions (UDFs) and Stored Procedures |
| Topic 2: Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Filtering, Aggregating, and Joining DataFrames - Window functions - Using built-in functions - Persisting transformed data |
| Topic 3: Snowpark Concepts | 15% | - Client-side vs. Server-side execution - Snowpark Sessions and connection management - Snowpark architecture and core concepts - Snowpark DataFrames and query plans - Stored procedures and conditional logic - Transformations vs. Actions |
| Topic 4: Performance Optimization and Best Practices | 20% | - Caching strategies - Debugging and explain plans - Warehouse sizing for Snowpark - Vectorized UDFs - Query pushdown and optimization - Minimizing data transfer |
Snowflake Certified SnowPro Specialty - Snowpark FAQs: What Candidates Ask Before Exam Day
Yes. iPassleader offers a free PDF demo of the SPS-C01 practice questions so you can check the quality, the format, and the difficulty level before you spend anything. After you purchase, you also receive 365 days of free updates, and if the product expires you can extend the update service at a 50% discount from your member zone.
The SPS-C01 exam contains 55 questions and gives you 85 minutes to complete them. That works out to a brisk pace, so you cannot afford to stall on a single tricky item — flag it, move on, and circle back if time allows. Before exam day, run at least two timed mock sessions with the iPassleader desktop or online test engine; practicing against a real clock turns the official time limit into a comfortable rhythm instead of a source of panic.
The SPS-C01 exam (full name: Snowflake Certified SnowPro Specialty - Snowpark) is a Snowflake certification exam that leads to the Snowflake Certification credential, positioned at the Specialty level. It validates the skills Snowflake expects from professionals working with its technologies, and passing it is the key step toward getting certified. It also sits within a broader certification path that includes SnowPro Core Certification.
The Snowflake Certified SnowPro Specialty - Snowpark exam is organized into 4 domains. The main areas include Performance Optimization and Best Practices (20%), Snowpark API for Python (30%), and Snowpark Concepts (15%). Scroll up to the exam syllabus section on this page for the complete topic-by-topic outline.
SnowPro Core Certification is required.
Eligibility requirements can change, so before you register, confirm the latest criteria on the official Snowflake exam page.
To pass the SPS-C01 exam you need a score of 750 (scaled 0-1000), and the official registration fee is $225 USD. Keep in mind that a failed attempt comes at full price: retaking the exam means paying the entire fee again. A smart way to protect that investment is to self-test first — work through the 374 practice questions at iPassleader until your timed scores sit comfortably above the passing line before you book a seat.
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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?
- A. Create an external function and grant access to the API integration that provides the security context.
- B. Create a UDF and grant USAGE on the stage to the role that owns the UDF.
- C. Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
- D. Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
- E. Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
Correct Answer: E 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?
- A.
- B.
- C.
- D.
- E.
Correct Answer: C,E 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
You are working with a Snowpark DataFrame 'transactions df that contains customer transaction data'. This data includes a 'transaction amount' column and a 'transaction date' column. You need to create a new feature called 'is weekend transaction' that indicates whether a transaction occurred on a weekend (Saturday or Sunday). Furthermore, some 'transaction_date' values are missing. You want to impute the missing dates with the mode (most frequent date) before determining if the transaction occurred on a weekend. Which of the following steps, when combined, provide the correct and most efficient approach to achieve this?
- A. 1. Calculate the mode of the 'transaction_date' column. 2. Filter all rows where 'transaction_date' is null and load that data into a temporary table. 3. Update all rows in original 'transactions_df from temporary table. 4. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 5. Apply the UDF to the 'transaction_date' column to create the column.
- B. 1. Replace the null values in 'transaction_date' column with a constant string like '1900-01-01'.2. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 3. Apply the UDF to the 'transaction_dates column to create the column. 4. After applying the UDF convert back the replaced values in transaction_date to null.
- C. 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 3. Create a UDF using datetime library that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the column.
- D. 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 'fillna()'. 3. Use the 'dayofweek' function to determine the day of the week and create using a 'when' condition.
- E. 1. Calculate the mode of the 'transaction_date' column. 2. Fill the missing values in the 'transaction_date' column with the calculated mode. 3. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the 'is weekend transaction' column.
Correct Answer: D 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?
- A. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
- B. GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
- C. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST,
- D. GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
- E. GRANT ALL PRIVILEGES ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON SCHEMA ANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
Correct Answer: C 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
Consider a Snowflake table 'sales_data' with a VARIANT column 'order_details' containing an array of JSON objects, where each object represents an item in an order. Each item object has fields like 'quantity', and 'price'. You need to calculate the total price for each order by summing the product of 'quantity' and 'price' for all items in the 'order_details' array. Which of the following Snowpark Python snippets correctly accomplishes this?
- A.
- B.
- C.
- D.
- E.
Correct Answer: D 🗳️
Explanation: Only visible for iPassleader members. You can sign-up / login (it's free).
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