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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question #1
You have a Snowflake stage containing image files. You need to write a Snowpark Python application that extracts metadata (e.g., image resolution, format) from these images and stores the metadata in a Snowflake table. You want to leverage a Python library, such as Pillow (PIL), for image processing. Which of the following steps are necessary to correctly and efficiently implement this?

A. Download all the image files to the Snowpark client, process them locally using Pillow, and then upload the extracted metadata to Snowflake using session
B. Create a Python UDF (User-Defined Function) that uses Pillow to extract metadata from the image files. Register the UDF with Snowflake. In a Snowpark DataFrame transformation, call the UDF for each image file to extract the metadata. Finally, write the resulting DataFrame to a Snowflake table.
C. Create a Conda environment specification file ('environment.yml') that includes Pillow as a dependency. Upload the 'environment.ymr file to a Snowflake stage. Use 'session.add_packages' in the Snowpark session to load the Pillow library. Read the image files using , process them with Pillow, and then write the metadata to a Snowflake table using 'session.write_pandas()'.
D. Upload the Pillow library as a zip file to a Snowflake internal stage. Create a Snowpark stored procedure. In the stored procedure code, import the Pillow library using 'import zipfile; sys.path.append('pillow.zip'); from PIL import Image'. Read the image files using , process them with Pillow to extract metadata, and then insert the metadata into the Snowflake table.
E. Use Snowpark's built-in image processing functions to extract metadata directly from the image files. This eliminates the need for external libraries like Pillow.

Question #2
Consider the following Snowpark Python code snippet:

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

E. This code requires a configured Anaconda environment to run successfully.

Question #3
You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

A. Implement a single function that uses a series of 'if/elif/else' statements to determine the file type and location, then calls the appropriate 'session.read' method with the corresponding options.
B. Create separate functions for each file type and location combination (e.g.,
C. Use the 'session.sqr method with dynamically generated SQL queries that include the file format and location details. Construct the SQL query string based on the input parameters.
D. Create a class hierarchy with an abstract base class 'DataFrameReader' that defines a 'read_file' method. Implement subclasses for each file format and location, overriding the 'read_file' method with the specific logic for that format and location.
E. Create a generic function str, file_format: str, options: dicty that uses 'getattr(session.read, file format)' to dynamically call the appropriate 'session.read' method based on the 'file_format' parameter. Pass additional configuration through the 'options' dictionary.

Question #4
You are tasked with operationalizing a Snowpark Python UDF for batch scoring of a large dataset. The UDF takes a set of feature columns and returns a prediction. You want to optimize performance and resource utilization. Select all the strategies that would effectively improve the operational efficiency and scalability of your UDF execution.

A. Utilize the 'vectorized' argument during UDF registration to enable batch processing of input data within the UDF.
B. Adjust the 'MAX BATCH SIZE parameter for the warehouse executing the UDF to the largest possible value to minimize overhead.
C. If the UDF performs external API calls, implement retry logic with exponential backoff to handle transient network errors gracefully.
D. Ensure that the Snowpark DataFrame being passed to the UDF is appropriately partitioned based on a relevant column (e.g., a geographical region) before invoking the UDF.
E. Always use a warehouse size of 'X-Large' or larger regardless of the data volume to guarantee sufficient resources for UDF execution.

Question #5
You are building a Snowpark application that requires you to connect to Snowflake from an environment where directly specifying credentials in the code is not permitted for security reasons. Which of the following are valid and recommended ways to securely pass authentication information to the Snowpark Session?

A. Using the Snowflake CLI's 'snowflake configure' command and relying on the A.snowflake/config' file. This is suitable for development but not recommended for production due to local file dependency.
B. Hardcoding the credentials in the Snowpark Python script and obfuscating them using Base64 encoding. This provides security by obscurity, making it a reasonably secure approach.
C. Using environment variables and retrieving them using 'os.environ' to build the connection parameters. This is a secure and recommended approach.
D. Storing credentials in a dedicated secret management service (e.g., HashiCorp Vault, AWS Secrets Manager) and retrieving them using an appropriate API. This is the most secure and recommended approach for production environments.
E. Storing credentials in a Snowflake stage and retrieving them from there at runtime. This is an acceptable, though more complex, solution.

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