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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: DataFrame Operations and Data Processing | - Data transformation workflows
|
| Topic 2: Data Engineering with Snowpark | - Pipeline development
|
| Topic 3: Testing, Debugging, and Deployment | - Production readiness
|
| Topic 4: User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
| Topic 5: Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| Topic 6: Snowpark Fundamentals | - Snowpark architecture and concepts
|
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
You are developing a Snowpark application that needs to read data from a set of CSV files stored in a Snowflake stage named ' my_stage'. The files have a header row and are comma-delimited. You want to use the Snowpark API to create a DataFrame from these files, automatically inferring the schema. Which of the following code snippets correctly achieves this?
- A.
- B.
- C.
- D.
- E.
Correct Answer: A 🗳️
Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).
You are developing a Snowpark Python stored procedure that utilizes external Python libraries (e.g., 'requests', 'numpy'). What are the recommended steps for deploying this stored procedure to Snowflake, ensuring that all necessary dependencies are available during execution?
- A. List all required dependencies in the 'packages' parameter when creating the stored procedure. Snowflake automatically resolves and installs these dependencies from its managed Anaconda channel.
- B. None of the above, Snowflake does not support external python libraries in stored procedures.
- C. Package all required dependencies into a ZIP file and upload it to a Snowflake stage. Specify the stage path in the 'imports' parameter when creating the stored procedure.
- D. Both A and B are correct methods to deploy the stored procedure.
- E. Include the installation commands (e.g., 'pip install requestS , 'pip install numpy') directly within the stored procedure's Python code. Snowflake will execute these commands during each invocation of the stored procedure.
Correct Answer: D 🗳️
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You are tasked with creating a secure UDF in Snowflake using Snowpark Python API that encrypts sensitive customer data before storing it. The UDF should be accessible only to users with specific roles. You have the following code snippet. What needs to be done to make it secure and operationalize for multiple users?
Considering Security best practices, what steps are critical for securing the UDF and granting appropriate permissions to other users?
- A. Grant EXECUTE privilege on the UDF and USAGE privilege on the database and schema containing the UDF to the roles needing access.
- B. Use Snowflake's Secret object to securely store the encryption key and retrieve it within the UDF. Grant USAGE privilege on the secret to specific roles.
- C. Store the encryption key directly in the UDF code for simplicity.
- D. Grant global USAGE privilege on the function's stage location to all users.
- E. Bypass role-based access control and use shared credentials for UDF execution.
Correct Answer: A,B 🗳️
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You have a Snowpark DataFrame named with columns 'category', , and You want to perform the following transformations using Snowpark:
- A.
- B.
- C.
- D.
- E.
Correct Answer: D 🗳️
Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).
You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?
- A.
- B.
- C.
- D.
- E.
Correct Answer: D 🗳️
Explanation: Only visible for TestPassKing members. You can sign-up / login (it's free).
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