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Snowflake SPS-C01 Q&A - in .pdf
- Exam Code: SPS-C01
- Exam Name: Snowflake Certified SnowPro Specialty - Snowpark
- Updated: Oct 26, 2026
- Q & A: 374 Questions and Answers
- Convenient, easy to study.
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- Q & A: 374 Questions and Answers
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- Exam Code: SPS-C01
- Exam Name: Snowflake Certified SnowPro Specialty - Snowpark
- Updated: Oct 26, 2026
- Q & A: 374 Questions and Answers
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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and DataFrame Operations | 35% | - Complex data pipelines - Window functions - Persisting transformed data - Using built-in functions - Filtering, Aggregating, and Joining DataFrames |
| Topic 2: Performance Optimization and Best Practices | 20% | - Caching strategies - Warehouse sizing for Snowpark - Vectorized UDFs - Debugging and explain plans - Minimizing data transfer - Query pushdown and optimization |
| Topic 3: Snowpark Concepts | 15% | - Stored procedures and conditional logic - Client-side vs. Server-side execution - Transformations vs. Actions - Snowpark DataFrames and query plans - Snowpark architecture and core concepts - Snowpark Sessions and connection management |
| Topic 4: Snowpark API for Python | 30% | - DataFrame creation and manipulation - Reading and writing data - Working with Semi-structured data - User-Defined Functions (UDFs) and Stored Procedures - Establishing connections and session management |
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
Question #1
You have a Snowpark Python application that performs several data transformations on a DataFrame representing customer transactions. The application is experiencing performance issues, and you suspect that some transformations are unnecessarily expensive. Which of the following techniques can MOST effectively optimize the performance of your Snowpark application, specifically focusing on minimizing data movement and leveraging Snowflake's query optimization capabilities?
- A. Use User-Defined Functions (UDFs) written in Python for all transformations, regardless of their complexity.
- B. Always use the largest available Snowflake warehouse size to ensure sufficient compute resources.
- C. Take the dataframe to Pandas dataframe as soon as possible in between transformations, since Pandas dataframes will be faster.
- D. Explicitly call .cache()' on the DataFrame after each transformation to materialize intermediate results in memory.
- E. Leverage Snowpark's built-in DataFrame transformations (e.g., .groupBy()') to allow Snowflake to optimize the query execution plan. Avoid pulling large amounts of data into the client application for simple operations. Only call 'collect()' as the last and final option, as this is the most costly activity of all.
Correct Answer: E 🗳️
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Question #2
A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?
- A. GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
- B. GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
- C. GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
- D. GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
- E. GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
Correct Answer: A 🗳️
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Question #3
You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?
- A.
- B.
- C.
- D.
- E.
Correct Answer: A,D 🗳️
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Question #4
You have a Python function, 'calculate metrics(df: snowpark.DataFrame, metric name: str) -> snowpark.DataFrame', that calculates various metrics on a Snowpark DataFrame. You want to deploy this function as a stored procedure in Snowflake. You need to ensure that the stored procedure has appropriate permissions to read data from a table named 'customer data' and write results to a table named 'metrics_table'. Which of the following steps are necessary to achieve this, assuming you are using the 'session.sproc.register' method?
- A. Grant the 'USAGE privilege on the database and schema containing the 'customer_data' and 'metrics_table' tables to the role executing the stored procedure.
- B. When registering the stored procedure using 'session.sproc.register' , specify the argument and provide a 'replace=True' if necessary. This will allow you to assign ownership of the stored procedure to a role with the necessary privileges.
- C. Grant the 'SELECT privilege on the 'customer_data' table and the 'INSERT privilege on the 'metrics_table' table to the role executing the stored procedure.
- D. Specify the 'imports' argument in 'session.sproc.register' with the list of packages which are needed to run 'calculate_metrics' function.
- E. Specify the 'packages' argument in 'session.sproc.register' to include any Python dependencies required by the 'calculate_metrics' function.
Correct Answer: B,C,E 🗳️
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Question #5
You have a Snowpark DataFrame named 'products' with columns 'product_id' (INT), 'product_name' (STRING), and 'price' (DOUBLE). You want to apply a transformation to calculate a 'discounted_price' column, which is the 'price' reduced by 10% if the price is greater than $100.00. Which of the following code snippets is the most efficient way to achieve this using Snowpark Python?
- A.
- B.
- C.
- D.
- E.
Correct Answer: A,D 🗳️
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