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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet designed to calculate a custom metric on financial data, using a vectorized UDF for performance. Identify potential performance bottlenecks and recommend optimization strategies.

Which of the following actions (may be more than one) would MOST likely improve the performance of this Snowpark application?

A) Change 'FloatType' to 'DoubleType' as it provides more precision.
B) Use to explicitly declare the Pandas dependency.
C) Ensure that the 'financial_data' table is clustered by 'ticker_symbor to optimize the 'groupBy' operation.
D) Repartition the 'data' DataFrame by 'ticker_symbol' before the 'groupBy' operation using to improve data locality.
E) Use before writing to the 'metric_table' , especially if the table is subsequently used downstream.

2. You have a Snowpark DataFrame containing product information, and you want to persist it into a Snowflake table named PRODUCTS. You need to handle the following scenarios: 1. If the table 'PRODUCTS does not exist, create it. 2. If the table PRODUCTS' exists, append the data from 'df_products' to it. Which of the following methods can achieve this?

A)

B)

C)

D)

E)

3. You have a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF relies on a pre-trained machine learning model stored as a file in a Snowflake stage. To enhance security, you want to create a secure UDF. Which of the following steps are necessary to achieve this?

A) When creating the UDF, specify 'secure=True' in the 'CREATE FUNCTION' statement, and explicitly grant USAGE privilege on the stage containing the model file to the role that executes the UDF using 'GRANT USAGE ON STAGE TO ROLE
B) Ensure the function definition specifies a 'context' parameter to pass security context.
C) Grant USAGE privilege on the stage containing the model file to the SNOWFLAKE.DATA_GOVERNANCE role.
D) Wrap the UDF creation in a stored procedure with 'EXECUTE AS CALLER to elevate privileges and ensure model access.
E) Grant READ privilege on the stage containing the model file to the role that owns the secure UDF.

4. Consider a scenario where you have a table 'EMPLOYEES' with columns 'employee id', 'department', and 'salary'. You want to delete employees who belong to either the 'HR' or 'Finance' department and have a salary less than 60000. Which of the following Snowpark DataFrame operations correctly implements this deletion?

A) Option A
B) Option C
C) Option B
D) Option E
E) Option D

5. 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?

A) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
B) Create a UDF and grant USAGE on the stage to the role that owns the UDF.
C) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
D) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
E) Create an external function and grant access to the API integration that provides the security context.

Solutions: