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Snowflake SPS-C01 Exam Overview:

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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

You are tasked with optimizing a Snowpark application that uses a Python UDF to perform complex string manipulations on a large dataset. The current implementation uses a scalar UDF. You are considering converting it to a vectorized UDF. What are the key considerations and potential limitations you need to address during the conversion to ensure correctness and optimal performance? Choose all that apply:

A. The input and output data types of the vectorized UDF must exactly match the corresponding column data types in the Snowpark DataFrame.
B. Vectorized UDFs always perform better than scalar UDFs, regardless of the complexity of the string manipulations or the size of the dataset.
C. The vectorized UDF's return type must be compatible with Snowpark's data types, and the UDF should return an array of the appropriate type with the same length as the input arrays.
D. The vectorized UDF must be able to handle NULL values gracefully within the input arrays, as these can cause errors if not explicitly addressed.
E. The vectorized UDF should utilize libraries like NumPy or Pandas for efficient array processing, but it's important to be aware of the limitations on available Python packages in the Snowflake environment.

Question 2

You are tasked with building a Snowpark application to perform sentiment analysis on customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS'. The application should be deployed as a UDF. The sentiment analysis is performed by a third-party Python library, 'sentiment_analyzer'. Due to security constraints, direct internet access is prohibited from within the Snowflake environment. What steps are necessary to ensure the 'sentiment_analyzer' library can be used by your Snowpark UDF?

A. Install the 'sentiment_analyzer' library using 'conda install' directly within the Snowpark session before creating the UDF.
B. Request Snowflake support to whitelist the 'sentiment_analyzer' library for direct download during UDF execution.
C. Package the 'sentiment_analyzer' library into a JAR file and upload it to a Snowflake stage, then specify the JAR in the 'imports' parameter of the UDF creation statement.
D. Package the 'sentiment_analyzer' library into a ZIP file and upload it to a Snowflake stage, then specify the ZIP in the 'imports' parameter of the UDF creation statement.
E. Use the 'packages' parameter in the UDF creation statement to specify the 'sentiment_analyzer' library from Anaconda.

Question 3

You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A. Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
B. Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
C. Create an external function and grant access to the API integration that provides the security context.
D. Create a UDF and grant USAGE on the stage to the role that owns the UDF.
E. Create a secure UDF and use the function to access stage credentials within the UDF's handler function.

Question 4

You are developing a Snowpark application that uses a UDF written in Python. This UDF requires several third-party Python packages (e.g., 'pandas', 'scikit-learn'). You need to ensure these packages are available in the UDF's execution environment within Snowflake. You have the packages listed in a 'requirements.txt' file. Which of the following methods, alone or in combination, correctly provision these dependencies for the UDF? (Select all that apply)

A. Create a Conda environment with the required packages and upload the resulting environment file to a Snowflake stage. Specify the stage location in the 'conda_integration' argument of the 'session.udf.register' method or guff decorator.
B. Upload the 'requirements.txt file to a Snowflake stage and specify the stage location in the 'imports' argument of the '@udf decorator.
C. Use the 'session.add_packages' method to add the packages programmatically before registering the UDF.
D. Include the packages directly in the '@udf decorator's 'packages' argument as strings (e.g., 'scikit-learnT).
E. Manually install the packages on the Snowflake compute warehouse nodes using SSH.

Question 5

Consider the following scenario: You need to implement a UDF in Snowpark Python to calculate the distance between two geographical coordinates (latitude and longitude). The UDF should handle potential null values gracefully and return null if either input coordinate is null. Which code snippet demonstrates the MOST efficient and correct implementation, leveraging Snowpark's capabilities?

A.

B.

C.

D.

E.

Solutions: