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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?

A)

B)

C)

D)

E)

2. A data engineering team is building a Snowpark pipeline to process IoT sensor data'. They want to create a UDF that uses a 3rd-party Python library (not available in Snowflake's Anaconda channel) to analyze the sensor readings. The UDF needs to be efficiently deployed and managed within Snowflake. Which of the following approaches represents the MOST robust and scalable way to register and deploy this UDF using Snowpark?

A) Create a virtual environment with the necessary Python library, zip it, upload the zip file to a Snowflake stage, and use to register the UDF. Reference the stage location and virtual environment in the register call.
B) Use 'session.add_packages' to add the specific Python package directly from the Snowflake Anaconda channel (even if the required version isn't available) and then use 'session.udf.register' for the UDF definition.
C) Create a Docker container with the Python library, push it to Snowflake Container Services, and call this container from the UDF.
D) Use 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.
E) Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.

3. You are tasked with creating a Snowpark session that utilizes a specific Snowflake warehouse for all operations. Which of the following code snippets BEST demonstrates how to correctly specify the 'warehouse' parameter when creating a session using snowpark.Session.builder.configs'?

A)

B)

C)

D)

E)

4. You are tasked with optimizing a Snowpark Python application that performs complex data transformations using a large DataFrame. The application is running slower than expected. You suspect that data skew is causing uneven distribution of work across the Snowflake warehouse nodes. Which of the following techniques could be used to mitigate data skew and improve the performance of your Snowpark application? (Select TWO)

A) Use the function to sort the data before performing the transformations.
B) Utilize Snowflake's automatic clustering feature on the underlying table to improve data locality.
C) Use the function with the 'BROADCAST' strategy for smaller DataFrames that are joined with the large DataFrame.
D) Increase the warehouse size to the largest possible option.
E) Use the function to redistribute the data evenly across the warehouse nodes based on a specific column or set of columns.

5. 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)?

A)

B)

C)

D)

E)

Solutions: