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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

A) Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
B) Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
C) Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.
D) Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.
E) Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.

2. A data engineering team has created several Snowpark Python UDFs and UDTFs in the 'TRANSFORMATIONS' schema of the 'ANALYTICS' database. A data science team needs to use these functions in their data analysis notebooks. What is the MINIMUM set of privileges that must be granted to the data science team's role ('DATA SCIENTIST') to allow them to discover and execute these UDFs and UDTFs?

A) GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
B) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT EXECUTE ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST,
C) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON ALL FUNCTIONS IN SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
D) GRANT ALL PRIVILEGES ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT ALL PRIVILEGES ON SCHEMA ANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;
E) GRANT USAGE ON DATABASE ANALYTICS TO ROLE DATA SCIENTIST; GRANT USAGE ON SCHEMAANALYTICS.TRANSFORMATIONS TO ROLE DATA SCIENTIST;

3. You have created a Snowpark UDF that uses a custom Python module 'my_module.py', containing a function 'process data'. This module is not available through Anaconda'. You've packaged the module into a zip file named 'my module.zip'. What steps are necessary to deploy this UDF in Snowflake so that it can correctly use the 'my_module'?

A) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
B) Upload 'my_module.zip' to an external stage (e.g., AWS S3 or Azure Blob Storage). Configure Snowflake to access the external stage. Create the UDF, specifying the external stage path in the 'imports' argument.
C) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'packages' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
D) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. No changes to sys.path are required within the UDF.
E) Upload 'my_module.zip' to an internal stage, then create the UDF using 'session.add_import' within the UDF definition, specifying the stage path. No additional configuration is needed.

4. You are using Snowflake Notebooks to develop a Snowpark application and want to leverage a custom Python library that is not available in the default environment. What steps are necessary to make this library available within your Snowflake Notebook?

A) Create a conda environment specification file ('environment.yml') that includes the custom library, upload it to a Snowflake stage, and then create a new environment based on that file when creating or modifying the Snowflake Notebook.
B) Install the library using pip in the Snowflake Notebook's terminal and then restart the Snowflake Notebook.
C) Install the library directly within the Snowflake Notebook using '!pip install
D) Create a deployment file using setup.py, upload deployment file to stage, and create function
E) Upload the Python library's ' .py' file directly to the Snowflake stage and import it using 'import sys; sys.path.append("); import

5. You are developing a Snowpark Python stored procedure for processing financial data'. The procedure uses the 'pandas' library for data manipulation and the 'scipy' library for statistical calculations. You want to optimize the execution of the stored procedure to leverage the available resources in your Snowflake environment. Which of the following strategies would be MOST effective in improving the performance of your stored procedure, considering the need to handle large datasets?

A) Increase the warehouse size to the largest available option (e.g., X-Large) to provide more memory and CPU resources.
B) Partition the input DataFrame into smaller chunks and process each chunk sequentially using Pandas, then combine the results.
C) Convert the Snowpark DataFrame to a Pandas DataFrame within the stored procedure and perform all calculations using Pandas and SciPy.
D) Utilize Snowpark's vectorized UDFs to perform the calculations on the data in parallel, avoiding the need to transfer the data to Pandas.
E) Use the 'cachetools' library within the stored procedure to cache intermediate results of calculations, reducing redundant computations.

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