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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

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

2. You have a Python function that takes a string as input and returns a sentiment score (a float between -1 and 1). This function relies on a large pre-trained Natural Language Processing (NLP) model. You want to deploy this function as a UDF in Snowpark and optimize its performance, specifically minimizing the model loading time for each execution. You have already uploaded the model to a stage named '@my_stage/models'. Select the option that combines caching techniques and UDF deployment strategies to achieve the best performance.

A)

B) Option B and C are best and gives a scalable solution
C)

D)

E)

3. You are developing a Snowpark Python stored procedure that utilizes external Python libraries (e.g., 'requests', 'numpy'). What are the recommended steps for deploying this stored procedure to Snowflake, ensuring that all necessary dependencies are available during execution?

A) Package all required dependencies into a ZIP file and upload it to a Snowflake stage. Specify the stage path in the 'imports' parameter when creating the stored procedure.
B) List all required dependencies in the 'packages' parameter when creating the stored procedure. Snowflake automatically resolves and installs these dependencies from its managed Anaconda channel.
C) None of the above, Snowflake does not support external python libraries in stored procedures.
D) Both A and B are correct methods to deploy the stored procedure.
E) Include the installation commands (e.g., 'pip install requestS , 'pip install numpy') directly within the stored procedure's Python code. Snowflake will execute these commands during each invocation of the stored procedure.

4. You are tasked with deploying a set of Python UDFs and UDTFs to a Snowflake environment using Snowpark. These functions rely on several external Python packages and need to be versioned and managed effectively. Which of the following strategies provides the MOST robust and scalable solution for managing dependencies and deploying these functions in a reproducible manner?

A) Creating a 'requirements.txt' file and including all required packages in it. Zipping this file and uploading it to a Snowflake stage, and specifying it in 'imports'.
B) Manually uploading the required Python packages to a Snowflake stage and specifying them in the 'packages' argument of the '@sf.UDF and 'session.udtf.register' calls. Update the packages on stage every time dependencies are upgraded.
C) Creating a 'requirements.txt' file, using 'conda' to create an environment.yml and use 'snowflake.snowpark.functions.udf and 'session.udtf.register' with the environment.yml. Update the environment.yml manually when dependencies are upgraded.
D) Options A,B and C are all equally viable options
E) Creating a conda environment specification file (environment.yml) that lists all dependencies, storing the environment.yml file in a Snowflake stage. Update the environment.yml when dependencies are upgraded.

5. You are working with sensor data in Snowpark. Your data contains (Integer), 'timestamp' (Timestamp), 'temperature' (Double), and 'status' (String). You need to create a Snowpark DataFrame named representing this data'. Which of the following is the most efficient and type-safe way to create the DataFrame from a list of Python tuples using an explicitly defined schema, assuming you need to maintain maximum precision for temperature readings and that all data types should map to the most efficient and appropriate Snowflake data type?

A)

B)

C)

D)

E)

Solutions: