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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with deploying a Snowpark Python application that utilizes a third-party library, 'scikit-learn' , for machine learning tasks. The application will be executed as a Snowflake Stored Procedure. What are the necessary steps to ensure the 'scikit-learn' library is available within the Snowpark environment?

A) Install scikit-learn on your local machine, package your snowpark code into a zip file and upload it to a stage, no extra steps are required.
B) Include the 'scikit-learn' library directly in the Snowpark session using 'session.add_import(sklearny.
C) Create a Snowflake Anaconda channel integration, add the 'scikit-learn' package to the channel, and then reference the channel in the Snowpark session configuration.
D) Create a Snowflake Anaconda environment using conda, include the 'scikit-learns package in the environment, and then create a Snowpark Stored Procedure that utilizes the environment via the 'packages' parameter in the CREATE PROCEDURE statement.
E) Upload the 'scikit-learrf library as a ZIP file to a Snowflake stage, create a Python UDF that unzips the library, and then import the library within the Snowpark Stored Procedure.

2. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
B) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
C) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
D) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.
E) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.

3. You are tasked with creating a Snowpark Python stored procedure that reads data from a Snowflake table, performs a complex data transformation using a 3rd party Python library (e.g., pandas, scikit-learn), and writes the transformed data to another Snowflake table.
The data transformation requires significant memory. You need to register this stored procedure in Snowflake. Which of the following approaches is the MOST appropriate for registering the stored procedure and managing the dependencies?

A) Create a conda environment file ('environment.yml') specifying the dependencies, upload it to a stage, and then use the '@sproc' decorator with the 'packages' argument referencing the conda environment.
B) Use the '@sproc' decorator without specifying any dependencies, assuming that the necessary libraries are pre-installed on the Snowflake worker nodes.
C) Install the required Python libraries directly on the Snowflake compute warehouse using a SQL command.
D) Create a Snowflake stage, upload the Python libraries as .zip files to the stage, and specify the stage path in the '@sproc' decorator's 'imports' parameter.
E) Use the function to add the required Python libraries before registering the stored procedure with the '@sproc' decorator.

4. You are building a Snowpark application that processes a large number of PDF files stored in a Snowflake stage. You need to extract text from each PDF file using a Python UDF and store the extracted text in a Snowflake table. You are considering different approaches for loading the PDF files into the UDE Which of the following approaches would provide the BEST performance and scalability, while minimizing network traffic and memory usage?

A) Use the 'snowflake.snowpark.functions.read' function in Python to read the PDF files directly from the stage within the UDF. This loads the file contents into a variable available for processing.
B) Use the 'GET_OBJECT Snowflake SQL command to retrieve each file's contents and then pass the results as arguments into the UDF for processing. This allows use of pure SQL statements to access the files.
C) Download all PDF files from the Snowflake stage to a local directory on the machine running the Snowpark application, then load the files from the local directory into the UDF.
D) Pass the file path of each PDF file in the Snowflake stage to the UDF as a string. Within the UDF, use the 'snowflake.snowpark.files.SnowflakeFile' class to open and read the file. This will allow efficient access to the file directly from the stage.
E) Load the PDF files into a pandas DataFrame within the Snowpark application, then pass the DataFrame to the UDF. This way the PDF can be available to all workers in the dataframe.

5. You have a Snowflake table named 'RAW EVENTS with a large number of events data, containing columns like 'EVENT ID', 'TIMESTAMP, 'USER ID, and 'EVENT_TYPE. The 'EVENT TYPE column contains string values representing different event categories. You want to create a Snowpark DataFrame, but due to the table's size, you only want to sample a small portion of the data for initial exploration and testing. Which of the following code snippets MOST accurately and efficiently creates a sampled Snowpark DataFrame named 'sampled_df containing approximately 1% of the rows from the 'RAW EVENTS table?

A)

B)

C)

D)

E)

Solutions: