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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) Use 'functions.udf and directly embed the package code within the UDF definition. This approach handles package management automatically.
D) Create a Docker container with the Python library, push it to Snowflake Container Services, and call this container from the UDF.
E) Use 'session.udf.register' and directly include the library code as a string within the UDF definition. This avoids external dependencies.

2. You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A)

B)

C)

D)

E)

3. You are tasked with optimizing the performance of a Snowpark Python application that performs complex data transformations on a large dataset of IoT sensor readings. The application uses a Snowpark-optimized warehouse. You notice that the application is consistently slow, with CPU utilization on the warehouse fluctuating significantly. Which of the following actions would be MOST effective in addressing this performance issue? Assume the dataset is partitioned on the 'sensor_id' column within Snowflake.

A) Enable auto-scaling on the warehouse with a minimum of 2 and maximum of 5 clusters. This will allow the warehouse to dynamically adjust capacity based on workload.
B) Rewrite the Snowpark DataFrame transformations using only built-in Snowpark functions and avoid using User-Defined Functions (UDFs) written in Python.
C) Repartition the Snowpark DataFrame using partition_expression='sensor_id')' before applying transformations. Then, explicitly colocate similar operations.
D) Increase the warehouse size to a larger instance (e.g., from X-Small to Small). This will provide more CPU and memory resources.
E) Ensure the Snowpark DataFrame transformations are pushed down to Snowflake as much as possible by avoiding actions like 'collect()' until absolutely necessary and leverage stored procedures.

4. You are working with Snowpark to create a DataFrame from a Python dictionary where keys represent column names and values are lists representing column data'. However, the dictionary contains lists of varying lengths for different columns. You need to create a DataFrame from the Python dictionary but are unsure how to create it. Which approach should you take and why?

A) Manually pad all lists in the dictionary with 'None' values until they have the same length. Then, create the DataFrame using 'session.createDataFrame(data)'.
B) Transform the dictionary into a list of dictionaries or tuples, padding the short lists with 'None' values. Then, define a schema and use 'session.createDataFrame(data, schema=schema)' to create the DataFrame.
C) Create a Pandas DataFrame from the dictionary first. Pandas handles lists of unequal lengths by filling the shorter lists with NaN. Then, convert the Pandas
D) Attempt to create the DataFrame directly using 'session.createDataFrame(data)'. Snowpark will automatically pad the shorter lists with 'NULL' values to match the length of the longest list.
E) DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Snowpark does not support creating DataFrames directly from dictionaries with lists of varying lengths. The code will throw an error. So, manually build the logic of combining the lists.

5. You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

A)

B)

C)

D)

E)

Solutions: