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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A) GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
B) GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
C) GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
D) GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
E) GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;

2. You are tasked with creating a Snowpark DataFrame from a series of large Parquet files stored in an external stage 'my_stage' . The files contain customer transaction data, but some files are corrupted and cause errors during DataFrame creation. You want to implement a solution that skips the corrupted files and logs the filenames of those files to a table named 'failed_files'. Assuming you have a Snowpark session 'session' and a UDF that inserts filenames into the 'failed_files' table, which of the following approaches is the MOST efficient and robust way to achieve this, while minimizing impact on performance and maintaining data integrity? Consider that you don't have direct control over the file format and data quality within the stage.

A) Implement a custom file listing function using 'session.sql('LlST to identify potentially corrupted files by checking file size or metadata, then exclude these files when creating the Snowpark DataFrame. Use 'session.read.parquet' with the filtered list of files.
B) Use 'session.read.parquet('stage://my_stage/ within a try-except block to catch errors. Inside the 'except' block, call with the filename. Retry the read operation for remaining files after removing the failing file from stage.
C) Create a Snowpark DataFrame using 'session.read.option('mode', 'PERMISSIVE').parquet('stage://my_stage/ ')'. This automatically skips corrupted records within valid files but doesn't handle entire corrupted files. Afterward, compare the counts of each file before and after processing to identify corrupted files based on lost records.
D) Use the command in Snowflake to load the Parquet files into a temporary table, specifying the 'ON ERROR = CONTINUE option. Then, create a Snowpark DataFrame from the temporary table. Log any rejected files using a 'VALIDATION MODE = RETURN ERRORS' copy command before creating the temporary table.

3. You have created a Python UDTF in Snowpark to process large volumes of image data'. This UDTF resizes each image and extracts certain features from it. The process is memory-intensive and sometimes fails due to Python process exceeding memory limits. You need to optimize this UDTF for memory usage. Which of the following strategies would be MOST effective? (Select TWO)

A) Utilize a smaller Snowflake warehouse size. Smaller warehouses have less memory per node, which will force the UDTF to process data in smaller chunks.
B) Implement lazy evaluation and iterators within the UDTF to process images one at a time instead of loading all images into memory at once.
C) Use a scalar UDF instead of a UDTF. Scalar UDFs are generally more memory efficient.
D) Leverage external packages such as PILIPillow with appropriate image compression techniques to reduce the memory footprint of each image before processing.
E) Increase the value of 'MAX BATCH SIZE in the Snowpark session configuration to allow the UDTF to process larger batches of images at once.

4. You are developing a Snowpark application that utilizes a UDF. You need to ensure that the UDF runs with the privileges of the caller (the user executing the query). Which of the following steps are necessary to accomplish this while creating the Snowpark session?

A) After creating the Snowpark session, execute the SQL command 'ALTER SESSION SET
B) The account administrator needs to explicitly grant the 'CREATE FUNCTION' privilege to the user.
C) No special steps are required when creating the Snowpark session; the UDF automatically inherits the caller's privileges.
D) When defining the UDF using Snowpark, ensure the argument is passed in the decorator. Create the Snowpark session as usual.
E) When creating the Snowpark session, explicitly set the 'privilege' parameter to 'CALLER.

5. 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) Use ALTER FUNCTION mydb.public.calculate_discount MODIFY AS with the new Python code block.
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 ADD PARAMETER promo_code STRING;' followed by 'ALTER FUNCTION mydb.public.calculate_discount SET BODY = 'new python code'; '
D) Use 'ALTER FUNCTION mydb.public.calculate_discount RENAME TO followed by creating a new UDF with the updated code and original name.
E) Snowflake does not allow modifying UDFs directly using SQL. You must redeploy the entire Snowpark application.

Solutions: