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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data scientist has developed a complex machine learning model in Python that needs to be operationalized within a Snowpark pipeline. This model depends on several custom Python packages not available in Snowflake's default environment. The data scientist wants to define a UDTF to apply this model to incoming data'. Which of the following steps are NECESSARY to successfully deploy and execute this UDTF in Snowflake? (Select three)

A) Specify the stage location in the 'imports' clause of the 'CREATE FUNCTION' statement when defining the UDTF.
B) Include only custom packages into the ZIP file and exclude common python library packages, as snowpark is pre-installed.
C) Create a virtual environment and install all the required Python packages.
D) Upload the ZIP file to a Snowflake stage.
E) Package the virtual environment as a ZIP file.

2. You have a Snowpark DataFrame named 'products_df' with columns 'product_id' (INT), 'product_name' (VARCHAR), and 'price' (FLOAT). You want to create a new DataFrame called 'discounted_products df that includes all columns from 'products_df' plus a new column named 'discounted_price', which is calculated as the original price minus a discount percentage specified by the variable 'discount_rate' (e.g., 0.1 for 10%). The 'discount_rate' is stored in the database table named 'discount_table'. You want to load the rate to variable. Choose the correct ways to achieve this. (Select all that apply)

A)

B)

C)

D)

E)

3. A Snowpark Python application is experiencing significant performance degradation when processing a large dataset (100GB+) stored in Snowflake. The application performs a complex series of transformations, including window functions and joins with smaller lookup tables. You suspect data skew is contributing to the issue. Which of the following strategies would be MOST effective in mitigating the impact of data skew and improving performance?

A) Convert the Snowpark DataFrame to a Pandas DataFrame before performing transformations.
B) Disable query result caching to ensure fresh data is always used.
C) Implement salting or pre-partitioning of the data based on a hash of the skewed column before performing the joins and window functions.
D) Increase the warehouse size to a larger instance (e.g., from X-Small to Large).
E) Cache the smaller lookup tables using 'session.createDataFrame' and broadcast them to all worker nodes.

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

5. You have developed a Snowpark application that uses a Python UDF to perform sentiment analysis on text data extracted from JSON files stored in a Snowflake stage. The UDF relies on a large pre-trained machine learning model that is loaded during the UDF initialization. After deploying the application, you observe that the UDF initialization is taking a significant amount of time, causing slow query performance. What are the three MOST effective strategies to optimize the UDF initialization time in this scenario?

A) Use the 'streamlit' library and its caching capabilities to cache loaded models. The UDF should call the streamlit api to retrieve the already loaded model.
B) Load the model outside the UDF definition within the Snowpark session, pass it as an argument to the UDF, then use the model as part of a vectorized UDF.
C) Utilize the 'context.add_dependency' method in Snowpark to specify the model file as a dependency. Snowflake will automatically distribute and cache the model file to the worker nodes.
D) Use the 'snowflake.snowpark.files.SnowflakeFile' class to load the model directly from the Snowflake stage within the UDF initializer, but only if the model is smaller than 256M
E) Use the 'cachetools' library to cache the loaded model within the UDF. This will help to avoid reloading the model every time the UDF is called.

Solutions: