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| Section | Objectives |
|---|---|
| Topic 1: Data Engineering with Snowpark | - Pipeline development
|
| Topic 2: Performance Optimization and Best Practices | - Efficient Snowpark execution
|
| Topic 3: DataFrame Operations and Data Processing | - Data transformation workflows
|
| Topic 4: Testing, Debugging, and Deployment | - Production readiness
|
| Topic 5: User Defined Functions and Stored Procedures | - Extending Snowpark with custom logic
|
| Topic 6: Snowpark Fundamentals | - Snowpark architecture and concepts
|
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NEW QUESTION # 283
You are tasked with building a data pipeline that uses Snowpark to process customer data in a table called 'CUSTOMERS'. The table contains sensitive information, and you need to ensure that any Personally Identifiable Information (PII) is removed from the table after 30 days. You decide to implement a mechanism to automatically delete records older than 30 days. Which of the following approaches would you consider when designing the deletion process with consideration of cost, performance and security?
Answer: A,B,E
Explanation:
Options B, C and E are viable and have tradeoffs. A and D will be less ideal. Option A : This approach can be very inefficient as it requires scanning the entire table daily to identify records for deletion. This can lead to high costs and performance issues. Option B : Using a Snowflake Task with a direct SQL DELETE statement is generally more efficient than reading the entire table in Snowpark, especially for large tables. Snowflake can optimize the DELETE operation internally. Option C : Encapsulating the deletion logic in a Snowpark Stored Procedure and scheduling it with a Snowflake Task allows you to leverage Snowpark's capabilities while benefiting from Snowflake's task scheduling and optimization. This can offer a good balance of flexibility and performance. Option D : This is an archive solution, not deletion; it's not as fast or space saving as truncating. Option E : Data masking is a secure alternative. It preserves the records while redacting sensitive data. No need to scan for old records, so its best on performance, cost, and security.
NEW QUESTION # 284
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?
Answer: B
Explanation:
Option E correctly defines a table function that takes a DataFrame as input and returns a DataFrame with the rolling average calculated using the 'avg()' function and a window specification. It then uses 'session.table_function' to apply the table function to the DataFrame and selects the required columns. Option A attempts to call directly within the table function definition which is invalid. Option B passes 'session' as the first argument to table function that's wrong. Option C defines that aren't compatible with DataFrame operations, this approach is not how you should do. Option D is same as C, and incorrect because, it attempts to use window functions inside the table function where the input is a table function input types.
NEW QUESTION # 285
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?
Answer: E
Explanation:
Option D is correct. It first selects the distinct 'customer_id' from both DataFrames. Then, it uses the 'minus' (or 'except_') set operation to find the difference between the customer IDs in the 'customers DataFrame and the customer IDs in the 'orders' DataFrame. This effectively returns the customer IDs of customers who have not placed any orders. The 'join' operation is not a set operation, and options B and C are not valid syntax for Snowpark. Option E is syntactically correct in Snowpark, and equivalent to Option D. However, since the question has to have one answer, Option D is kept.
NEW QUESTION # 286
You are working with a Snowpark DataFrame named 'employees_df' that contains employee data, including a column named 'hire_date' of type String. The date format in the 'hire_date' column is 'MM/DD/YYYY'. You need to convert this column to a DateType so you can perform date-based calculations. Which of the following Snowpark code snippets correctly converts the column to a DateType?
Answer: A
Explanation:
Option C is correct. The 'to_date' function in Snowpark requires the column to be converted as the first argument and the date format as the second argument to correctly parse the string representation of the date. Option A will attempt to convert, but since no format is provided it will fail or produce unexpected results, if the string format matches the default date format. Option B provides the wrong separator. Option D passes column name as string to 'to_date' function, it requires column object and Option E converts to 'TimestampType' , whereas we need 'DateType'.
NEW QUESTION # 287
You have a Snowpark Python application that performs several data transformations. You need to implement error handling to catch exceptions during DataFrame operations and log them to a Snowflake table named 'error _ log'. You also need to ensure that if any exception occurs, the application continues to run and processes the remaining data. Which of the following approaches (or combination of approaches) would be MOST suitable?
Answer: A,C
Explanation:
Options B and C, when combined, provide the most robust error handling. Wrapping individual DataFrame operations in try-except blocks (B) allows you to catch specific exceptions and log them appropriately, preventing a single error from crashing the entire application. Implementing a UDF with error handling (C) encapsulates complex logic and allows you to return both processed data and error messages, making it easier to identify and address issues. A global try-except block (A) might catch errors, but it doesn't provide granular control or context. Relying solely on Snowflake's built-in mechanisms (D) is insufficient for custom error logging and continuing execution. While a Snowpark logging API (E) would be ideal, such an API doesn't currently exist and relies on an assumption.
NEW QUESTION # 288
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