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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have created a Snowpark Python UDF named to apply discounts based on customer purchase history. You now need to modify the UDF to accept an additional parameter for promotional codes. However, direct modification of the code on stage is restricted. How can you alter this UDF using SQL, assuming the existing UDF definition resides in the 'mydb.public' schema?

A) Snowflake does not allow modifying UDFs directly using SQL. You must redeploy the entire Snowpark application.
B) Use 'CREATE OR REPLACE FUNCTION mydb.public.calculate_discount(order_total DOUBLE, customer_segment STRING, promo_code STRING) RETURNS DOUBLE LANGUAGE PYTHON ..: with the updated UDF definition.
C) Use 'ALTER FUNCTION mydb.public.calculate_discount RENAME TO followed by creating a new UDF with the updated code and original name.
D) Use ALTER FUNCTION mydb.public.calculate_discount ADD PARAMETER promo_code STRING;' followed by 'ALTER FUNCTION mydb.public.calculate_discount SET BODY = 'new python code'; '
E) Use ALTER FUNCTION mydb.public.calculate_discount MODIFY AS with the new Python code block.

2. You have a Snowpark Python application that interacts with Snowflake using a service account. You are rotating the private key associated with the service account. After updating the private key in your application's configuration, you encounter an error during the connection attempt: 'SnowflakeSQLException: 390103 (OSAOO): Failed to connect to DB. Encountered exception while creating connection: Authentication token has expired.' What is the MOST likely cause of this error, and what steps should you take to resolve it?

A) The connection string contains invalid characters. Ensure the account identifier and other parameters are correctly specified.
B) The Snowflake service account hasn't been granted sufficient permissions to access the required resources. Re-grant the necessary roles and privileges to the service account.
C) The private key is in an incorrect format. Ensure that the private key is in PKCS#8 format and is properly encoded.
D) The Snowflake cache still holds the old private key. Clear the Snowflake connection cache in the application by calling and restarting the application.
E) The public key associated with the new private key has not been authorized in Snowflake for the service account. Ensure that the public key is associated with the service account using ALTER SERVICE ACCOUNT SET RSA PUBLIC KEY =";'

3. You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

A) The warehouse being used for the Snowpark session is not configured with the feature enabled. Verify that the warehouse configuration includes = TRUE'.
B) The user role lacks the 'EXECUTE MANAGED TASK privilege. Grant this privilege to the user role executing the Snowpark application.
C) The dynamic table materialization schedule is too frequent, overwhelming the Snowflake warehouse. Increase the 'WAREHOUSE_SIZE' parameter of the dynamic table definition.
D) The Snowpark Python client version is outdated and does not support streaming dynamic tables. Upgrade to the latest version of the 'snowflake-snowpark- python' package.
E) The dynamic table definition contains unsupported SQL syntax, such as 'QUALIFY with complex window functions. Rewrite the dynamic table definition to use standard SQL constructs.

4. 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) 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()'.
B) 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.
C) 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.
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) Download all the image files to the Snowpark client, process them locally using Pillow, and then upload the extracted metadata to Snowflake using session

5. You are developing a Snowpark application that uses a UDTF written in Python to perform complex data transformations. The UDTF takes several input columns and returns multiple output columns. The data volume is very large. You observe performance bottlenecks during the UDTF execution. Which of the following strategies could you employ to optimize the performance of your UDTF? (Select TWO)

A) Reduce the number of input columns passed to the UDTF by performing some pre-processing outside the UDTF.
B) Use a scalar UDF instead of a UDTF to simplify the code and reduce overhead.
C) Avoid using UDTFs altogether and rewrite the transformation logic using built-in Snowpark DataFrame transformations, even if it makes the code significantly more complex.
D) Increase the warehouse size used for the Snowpark session to provide more computational resources.
E) Employ vectorized operations within the UDTF using libraries like NumPy or pandas to process data in batches.

Solutions: