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Last Updated: Nov 07, 2026
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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowpark API for Python | 30% | - DataFrame creation and manipulation - Working with Semi-structured data - Reading and writing data - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management |
| Topic 2: Snowpark Concepts | 15% | - Snowpark architecture and core concepts - Client-side vs. Server-side execution - Stored procedures and conditional logic - Snowpark DataFrames and query plans - Transformations vs. Actions - Snowpark Sessions and connection management |
| Topic 3: Performance Optimization and Best Practices | 20% | - Debugging and explain plans - Query pushdown and optimization - Minimizing data transfer - Caching strategies - Warehouse sizing for Snowpark - Vectorized UDFs |
| Topic 4: Data Transformations and DataFrame Operations | 35% | - Using built-in functions - Filtering, Aggregating, and Joining DataFrames - Complex data pipelines - Window functions - Persisting transformed data |
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
You have a Python function that takes a string as input and returns a sentiment score (a float between -1 and 1). This function relies on a large pre-trained Natural Language Processing (NLP) model. You want to deploy this function as a UDF in Snowpark and optimize its performance, specifically minimizing the model loading time for each execution. You have already uploaded the model to a stage named '@my_stage/models'. Select the option that combines caching techniques and UDF deployment strategies to achieve the best performance.
- A.
- B. Option B and C are best and gives a scalable solution
- C.
- D.
- E.
Correct Answer: B 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have a Snowpark DataFrame containing product information, and you want to persist it into a Snowflake table named PRODUCTS. You need to handle the following scenarios: 1. If the table 'PRODUCTS does not exist, create it. 2. If the table PRODUCTS' exists, append the data from 'df_products' to it. Which of the following methods can achieve this?
- A.
- B.
- C.
- D.
- E.
Correct Answer: E 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?
- A.
- B.
- C.
- D.
- E.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
A data engineer wants to create a Snowpark session using environment variables defined in a .env' file. The file contains the following: SNOWFLAKE ACCOUNT=myaccount.snowflakecomputing.com SNOWFLAKE USER=snowpark_user SNOWFLAKE SNOWFLAKE DATABASE=mydb SNOWFLAKE SCHEMA=myschema SNOWFLAKE WAREHOUSE=mywarehouse Which code snippet correctly establishes a Snowpark session using these environment variables?
- A.
- B.
- C.
- D.
- E.
Correct Answer: D 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have a Snowpark DataFrame named 'orders df with columns 'order id', 'customer id', 'order date', and 'total amount'. You need to create a new DataFrame that contains only the 'customer_id" and the total number of orders placed by each customer. However, you want to perform this aggregation in parallel using a user-defined function (UDF) to improve performance. Which approach is MOST efficient and CORRECT?
- A. Create a UDF that takes a customer ID as input and returns the count of orders for that customer, then apply this UDF to each distinct customer ID using 'map'.
- B. Use 'groupBy' to group by 'customer_id' and then use the 'count' aggregate function. Do not use a UDF.
- C. Create a UDF that calculates the mode of total_amount, then apply the UDF to the DataFrame using 'select'.
- D. Create a UDF that performs the entire aggregation and call it with 'orders_df. The UDF uses a Pandas DataFrame internally to perform the count.
- E. Create a UDF that takes an iterator of order IDs and returns the count. Apply this UDF to each partition of the DataFrame using 'maplnPartitions' .
Correct Answer: B 🗳️
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