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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark application that utilizes a vectorized Python UDF to perform complex calculations on a large dataset. You notice that the performance is still not optimal. You suspect that the bottleneck might be related to how the data is being partitioned and processed by Snowflake. Which of the following actions, when performed in conjunction with vectorization, would MOST likely improve performance?

A) Broadcast the DataFrame to all compute nodes before applying the UDF.
B) Repartition the Snowpark DataFrame using to align the data distribution with the computational needs of the UDF.
C) Convert the DataFrame to a Pandas DataFrame before applying the UDF.
D) Ensure that the data is pre-sorted according to the primary key of the table before applying the UDF.
E) Increase the number of UDF worker threads within the UDF definition.

2. You are developing a Snowpark application using Visual Studio Code and the Snowflake VS Code extension. You want to configure the extension to automatically detect and use a specific Anaconda environment for your Snowpark development. Assuming you have already created an Anaconda environment named 'snowpark_env', which configuration setting in the VS Code settings.json file would correctly specify the Python path for the Snowflake extension?

A) "python.defaultlnterpreterPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
B) "snowflake.python.defaultlnterpreterPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
C) "snowsql.pythonPath": "/path/to/anaconda3/envs/snowpark_env/bin/python"
D) "python.pythonPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"
E) "snowflake.snowpark.pythonPath": "Ipath/to/anaconda3/envs/snowpark_env/bin/python"

3. You are developing a Snowpark application that performs several complex transformations on a large DataFrame representing customer purchase history. This DataFrame is used multiple times in the application. You need to optimize the application's performance by caching the DataFrame. Which of the following approaches is the MOST efficient and memory-conscious way to cache the DataFrame in Snowpark?

A) Using to store the entire DataFrame in a Python list, then creating a new DataFrame from that list for each subsequent operation.
B) Creating a temporary table in Snowflake using and then reading it back into a new DataFrame for each subsequent operation.
C) Using immediately after the initial DataFrame creation.
D) Using without specifying a storage level. Snowflake will choose a default storage level.
E) Using 'session.createDataFrame(df.toPandas())' to convert the Snowpark DataFrame to Pandas and back to Snowpark DataFrame.

4. When using key pair authentication with Snowpark, what security best practices should you implement to protect your private key?
(Select all that apply)

A) Regularly rotate the key pair.
B) Grant broad access to the environment variable containing the private key to all developers.
C) Store the private key in an environment variable or a secure secret management system (e.g., HashiCorp Vault, AWS Secrets Manager, Azure Key Vault).
D) Store the private key directly in the Snowpark code repository.
E) Encrypt the private key at rest.

5. You have a Snowpark DataFrame containing sales data with columns 'region' , and 'sales_amount'. You need to calculate the total sales amount for each region and then filter the results to only include regions where the total sales amount is greater than 10000. Which of the following Snowpark code snippets correctly implements this logic?

A)

B)

C)

D)

E)

Solutions: