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Snowflake SPS-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Snowpark Concepts and Architecture | 25% | - Snowpark architecture and execution model
|
| Data Transformations and Operations | 35% | - Advanced operations
|
| Snowpark API and Development | 30% | - Multi-language support
|
| Performance and Best Practices | 10% | - Optimization techniques
|
Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.
- A.
- B.
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- D.
- E.
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A data engineering team is developing a Snowpark application that processes large volumes of JSON data'. They have created a UDF using Python that parses JSON strings and extracts specific fields. They need to deploy this UDF and ensure it can handle malformed JSON without causing the entire Snowpark job to fail. Which of the following strategies BEST addresses both the deployment and error handling requirements?
- A. Create a Java UDF instead of Python. Java has better JSON parsing libraries. Upload the JAR file to a stage and register the UDF with 'imports' clause referencing the JAR file. Use try-catch for error handling.
- B. Create an external function pointing to an AWS Lambda function that handles the JSON parsing. Configure the Lambda function to retry on failure, using SNS for notifications. No need to use ZIP file or any 'imports' clause.
- C. Create a Python UDF using the 'snowflake-snowpark-python' library and register it in Snowflake. Implement error handling using Snowflake's built-in 'ERROR HANDLING' clause in the 'CREATE FUNCTION' statement. Package the Python code as a ZIP file and upload it to a stage, using the 'imports' clause in the UDF definition.
- D. Create a Python UDF using the 'snowflake-snowpark-python' library and register it in Snowflake. Implement error handling using 'try-except block within the UDF to catch 'json.JSONDecodeError' exceptions and return NULL. Package the Python code as a ZIP file containing any necessary dependencies and upload it to a stage, using the 'imports' clause in the UDF definition.
- E. Create a Python UDF and register it in Snowflake, using 'try-except' block within the UDF to catch 'json.JSONDecodeError' exceptions. Package the Python code with the 'snowflake-snowpark-python' library.
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Consider the following Python code snippet using Snowpark:
Which of the following statements are true regarding this Snowpark code?
- A. The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
- B. The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
- C. The code establishes a connection to Snowflake using the provided credentials.
- D. The code reads data from a table named 'my_table' in Snowflake.
- E. The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
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A data engineer is tasked with calculating a 3-month rolling average of sales data using Snowpark Python. The sales data is stored in a table named 'SALES DATA' with columns 'sale_date' (DATE) and (NUMBER). They need to use a table function to accomplish this efficiently. Which of the following Snowpark Python code snippets correctly implements this rolling average calculation using a table function?
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- E.
Explanation: Only visible for Real4Prep members. You can sign-up / login (it's free).
A financial firm is using Snowpark Python to analyze stock trading data'. They have a DataFrame named 'trades' with columns 'trade_id', 'stock_symbol', 'trade_price', and 'trade_timestamp'. They want to identify potentially fraudulent trades based on the following criteria: 1. Trades where the 'trade_price' deviates significantly from the average price of that 'stock_symbol' over the past hour. 2. Trades originating from user accounts where the price is above $1000.3. Trades which has stock symbol 'XYZ'. The firm wants to apply multiple filters to the DataFrame to extract only the fraudulent trades and needs an efficient and concise approach using Snowpark. Which of the following code snippets, using 'trade_price' > 1000 as user identifier, MOST accurately and efficiently implements this filtering logic? Assume that a Snowflake user has a maximum amount they can spend on a trade, and therefore, the user ID is associated with 'trade_price'.
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- E.
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