Prepare with Snowflake SPS-C01 exam torrent, pass for sure

Updated: Jul 27, 2026

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Snowflake SPS-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:Snowflake Certified SnowPro Specialty - Snowpark
Exam Number:SPS-C01
Exam Format:Multiple Choice, Multiple Select, Interactive
Passing Score:750 (scaled 0-1000)
Real Exam Qty:55
Exam Price:$225 USD
Related Certifications:SnowPro Core Certification
Exam Duration:85 minutes
Available Languages:English
Sample Questions:Snowflake SPS-C01 Sample Questions
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:

SectionWeightObjectives
Topic 1: Snowpark API for Python30%- Reading and writing data
- Establishing connections and session management
- Working with Semi-structured data
- User-Defined Functions (UDFs) and Stored Procedures
- DataFrame creation and manipulation
Topic 2: Snowpark Concepts15%- Snowpark architecture and core concepts
- Snowpark DataFrames and query plans
- Stored procedures and conditional logic
- Transformations vs. Actions
- Snowpark Sessions and connection management
- Client-side vs. Server-side execution
Topic 3: Data Transformations and DataFrame Operations35%- Complex data pipelines
- Using built-in functions
- Persisting transformed data
- Window functions
- Filtering, Aggregating, and Joining DataFrames
Topic 4: Performance Optimization and Best Practices20%- Minimizing data transfer
- Debugging and explain plans
- Caching strategies
- Vectorized UDFs
- Query pushdown and optimization
- Warehouse sizing for Snowpark

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are setting up a VS Code development environment for Snowpark with the Snowflake extension. You want to ensure that you can securely authenticate to Snowflake and execute Snowpark code. Which of the following steps are essential to configure secure authentication within VS Code for Snowpark?

A) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use MFA. Ensure the username and password is provided with a valid MFA token.
B) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Snowflake Native Authentication. Ensure that the user has the required permissions to authenticate using this method.
C) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Key Pair authentication. Ensure the private key is securely stored and referenced in the connection settings.
D) Install the Snowflake VS Code extension and configure the Snowflake connection settings in the extension's configuration file using username and password.
E) Install the Snowflake VS Code extension and configure the connection settings to use OAuth. Ensure the OAuth client and secret are properly configured in Snowflake and referenced in the connection settings.


2. Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

A) Add in sproc.register.
B) Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.
C) Define 'session' as a global variable before calling 'session.sproc.register' .
D) Add 'session.close()' at the end of the main function.
E) Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.


3. You are working with a Snowpark DataFrame called 'customer df that contains customer data, including a column named 'registration_date' of data type TIMESTAMP NTZ. You need to filter the DataFrame to only include customers who registered in the year 2023. Which of the following Snowpark code snippets represents the MOST efficient and correct way to accomplish this filtering, considering potential timezone issues?

A)

B)

C)

D)

E)


4. You have two Snowpark DataFrames, 'customers' and 'orders'. The 'customers' DataFrame has columns and 'customer name'. The 'orders' DataFrame has columns 'order id', 'customer id', and 'order amount'. You need to find all customers who have NOT placed any orders. Which of the following Snowpark set operations correctly implements this?

A)

B)

C)

D)

E)


5. You have a Snowpark DataFrame with columns 'product_id', 'customer_id', and 'sale_amount'. Some values are negative, indicating returns, and others are null. You need to replace negative values with 0 and fill null values with the average 'sale_amount' for each 'product_id'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark?

A)

B)

C)

D)

E)


Solutions:

Question # 1
Answer: B,C,E
Question # 2
Answer: E
Question # 3
Answer: B
Question # 4
Answer: C
Question # 5
Answer: E

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