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

2. You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?

A)

B)

C)

D)

E)

3. You are working with a Snowpark DataFrame containing customer data, including a 'phone_number' column. Some phone numbers are missing or have incorrect formats. You want to impute missing values with a default phone number '000-000-0000' and remove any phone numbers that do not match the pattern 'XXX-XXX-XXXX' using Snowpark Python. Which of the following code snippets achieves this most efficiently?

A)

B)

C)

D)

E)

4. A data engineering team has deployed a Snowpark Python application that reads data from a Snowflake table, performs several complex transformations using Snowpark DataFrames, and writes the results back to another Snowflake table. The team is concerned about the cost associated with the virtual warehouse used by the Snowpark application. Which of the following strategies would be MOST effective in minimizing the virtual warehouse costs while maintaining acceptable performance?

A) Use serverless compute service when possible to avoid managing warehouse.
B) Optimize the Snowpark code to minimize data shuffling and reduce the amount of data processed.
C) Implement a resource monitor to limit the credit consumption of the virtual warehouse used by the Snowpark application.
D) Use the smallest possible virtual warehouse size (e.g., X-SMALL) and rely on Snowflake's automatic scaling capabilities to handle workload spikes.
E) Set the AUTO SUSPEND parameter of the virtual warehouse to the shortest possible duration (e.g., 60 seconds).

5. You're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?

A) The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
B) The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'
C) The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.
D) When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.
E) The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.

Solutions: