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| Section | Objectives |
|---|---|
| Topic 1: Testing, Debugging, and Deployment | - Production readiness
|
| Topic 2: DataFrame Operations and Data Processing | - Data transformation workflows
|
| Topic 3: Snowpark Fundamentals | - Snowpark architecture and concepts
|
| 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: Data Engineering with Snowpark | - Pipeline development
|
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NEW QUESTION # 219
You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?
Answer: A,E
Explanation:
Options B and E are correct. Option B correctly registers the function 'select_columnS as a stored procedure using "session.sproc.register'. Option E properly constructs the DataFrame by dynamically selecting columns by using 'df[col]'. Option A although syntactically correct may not perform as expected. Option C is incorrect because it attempts to use 'ArrayType' for a standard Python List, which is incompatible. Option D uses columns: str' which makes column as Tuple object instead of List object.
NEW QUESTION # 220
You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?
Answer: D
Explanation:
Secure UDFs are designed to execute with the privileges of the UDF owner, not the caller. allows secure access to stage credentials. Option B is incorrect because granting USAGE on the stage directly grants access to the data in the stage. Option C is a valid approach but unnecessary complex. Option D concerns external functions, not secure UDFs within Snowflake. Option E is incorrect because 'VOLATILE doesn't affect privilege handling.
NEW QUESTION # 221
A Snowpark application needs to authenticate to Snowflake using OAuth. The application is running on an Azure Function and uses a client ID, client secret, and refresh token obtained previously. Which of the following connection parameter dictionaries is correctly configured for OAuth authentication?
Answer: D
Explanation:
Option E correctly specifies 'oauth' as the authenticator and uses the access token to establish the connection. Using the access token directly after obtaining it is the common practice for OAuth in Snowpark. Options A, B, C and D are incorrect because for Snowpark, you typically use the access token, not the refresh token, directly in the connection parameters after you've initially exchanged the refresh token for an access token outside of this specific session establishment.
NEW QUESTION # 222
You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?
Answer: A,E
Explanation:
Option D and E are correct. Both implement the window function correctly to add the average salary per department as a new column and then sort the DataFrame as required. Option E is very close and just has the window partition defined with 'col('department')'. Option A is correct but uses sort. Option B is incorrect because it does not include the original columns other than department when grouping so will loose important data, and attempts to sort on a column that no longer exists.Option C uses 'avg('salary')' instead of avg(col('salary'))
NEW QUESTION # 223
You are working with a Snowpark DataFrame named 'sales df' containing sales data including columns 'product id', 'sale date', and 'sale_amount'. You want to create a new DataFrame 'filtered df that only includes rows where the 'product id' is present in a list of approved product IDs and the 'sale_amount' is greater than the average sale amount. You have already calculated the average sale amount and stored it in a variable named 'avg_sale amount'. Which of the following code snippets correctly achieves this?
Answer: A,C,D
Explanation:
Options B, C, and E are correct. Option B uses chained .filter() methods which is valid. Option C uses chained .where() methods which is functionally equivalent to filter. Option E correctly applies both conditions within the filter. Option A is incorrect because the '&' operator has different precedence compared to the bitwise & operator and can cause issues with Snowpark type checking; also, it does not have parentheses for the individual condition. Option D is incorrect because .contains() is typically for checking if a string contains a substring or if an array contains a specific value, not for checking if a value is in a list.
NEW QUESTION # 224
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