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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with optimizing a Snowpark Python application that performs complex data transformations on a large dataset. The application is running slower than expected, and you suspect that data serialization and transfer between the Snowpark client and the Snowflake engine are bottlenecks. Which of the following strategies could you implement to improve performance? (Select all that apply.)

A) Utilize smaller batch sizes when writing data back to Snowflake to reduce memory pressure on the client.
B) Minimize the amount of data transferred between the client and the engine by pushing down as much computation as possible to Snowflake using Snowpark DataFrame operations.
C) Create and utilize temporary tables within Snowflake to store intermediate results of complex transformations.
D) Increase the configuration parameter to maximize parallelism within the Snowpark engine without considering resources or potential bottleneck.
E) Convert all dataframes to Pandas dataframes locally and perform data manipulation with Pandas methods to take advantage of local resources.

2. You are tasked with optimizing a Snowpark application that performs complex data transformations on a large dataset (1 TB) stored in Snowflake. The application currently uses Snowpark DataFrames and is experiencing slow performance. You suspect the issue might be related to data transfer overhead between the Snowflake engine and the Python environment. Which of the following strategies would be MOST effective in minimizing this overhead and improving performance?

A) Reduce the data volume by applying aggressive filtering and aggregation using Snowpark DataFrame operations before any other transformations, minimizing the amount of data transferred.
B) Utilize User-Defined Functions (UDFs) written in Python to encapsulate the transformations and execute them within the Snowflake engine.
C) Increase the virtual warehouse size to the largest available option (e.g., X-Large) to improve processing power within Snowflake, regardless of data transfer costs.
D) Convert the Snowpark DataFrame to a Pandas DataFrame and perform the transformations locally within the Python environment.
E) Implement vectorization techniques within the Snowpark DataFrame operations using built-in functions and optimized expressions where applicable.

3. Consider the following Snowpark Python code snippet:

A) This code requires a configured Anaconda environment to run successfully.
B) The code demonstrates the Snowpark architecture, where transformations are translated into SQL and executed in Snowflake's engine. Only the final 'collect()' brings the results back to the client.
C)

D) The function will retrieve all rows from the 'customers' table and store them in a local Pandas DataFrame before applying the function.
E) The 'upper()' function will be executed on the client-side (where the Python code is running) for each row in the 'customers' table.

4. A Snowpark application is configured to connect to Snowflake using environment variables for authentication. However, the application frequently encounters intermittent connection errors. You suspect that the environment variables are not being correctly accessed by the Snowpark session builder. Which of the following actions could help diagnose and resolve the issue? (Select TWO)

A) Use to explicitly set connection parameters instead of relying on environment variables for debugging.
B) Explicitly print the values of the relevant environment variables (e.g., 'SNOWFLAKE_USER , 'SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) within the Snowpark application before attempting to create the session.
C) Replace 'session.builder.getorcreate()' with 'Session.builder.appName('MySnowparkApp').create()'
D) Verify that the user account specified in the environment variables has the necessary permissions (e.g., USAGE on the warehouse, database, and schema) in Snowflake.
E) Check that the environment variables are set at the correct scope (e.g., system-level, user-level, or within the application's execution environment) and are accessible to the Python process running the Snowpark application.

5. You are setting up a development environment for Snowpark using Anaconda and encounter the following error: 'ModuleNotFoundError: No module named 'snowflake.snowpark". You have already installed the package using pip. What is the MOST likely cause of this error and how do you resolve it?

A) The Snowflake driver is not installed. Install the Snowflake driver using 'pip install snowflake-connector-python'
B) The 'snowflake-snowpark-python' package was installed in a different Anaconda environment than the one you are currently using. Ensure you are in the correct environment when running your Snowpark code.
C) The Snowflake account identifier is not properly configured in your environment variables. Verify that 'SNOWFLAKE ACCOUNT is correctly set.
D) The Anaconda environment is not activated. Activate the environment using 'conda activate
E) The 'snowflake-snowpark-python' package is not compatible with the version of Python installed in your Anaconda environment. Upgrade Python to the latest version.

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