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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) Use ALTER FUNCTION mydb.public.calculate_discount MODIFY AS with the new Python code block.
B) Snowflake does not allow modifying UDFs directly using SQL. You must redeploy the entire Snowpark application.
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 '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.

2. You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?

A) Store the API key as an environment variable within the Snowflake warehouse configuration.
B) Store the API key as a constant string within the stored procedure's code.
C) Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.
D) Store the API key in a Snowflake table and query it within the stored procedure.
E) Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX

3. You're designing a Snowpark application to process large CSV files containing sensor data'. Each CSV file has millions of rows, and you need to calculate aggregate statistics (e.g., average, min, max) for specific sensor readings. The processing needs to be highly parallelized for performance. You have the following code snippet (incomplete):

Which of the following code snippets, when inserted at the ' ??? Add code here to calculate aggregate statistics ??? ' marker, would correctly calculate the average, minimum, and maximum readings for a column named 'sensor value' and return the result in a new Snowpark DataFrame?

A)

B)

C)

D)

E)

4. You have a Snowflake table 'PRODUCT REVIEWS' with columns 'product_id', 'review_text', 'review_date', and 'sentiment_score'. You are using Snowpark to build a data pipeline that analyzes these reviews and identifies potentially malicious or spam reviews. Based on a machine learning model deployed as a Snowflake UDF, you have identified a set of 'product_id' values that are associated with malicious reviews, and are stored in a python list called 'malicious_product_ids'. You need to delete all rows from the 'PRODUCT REVIEWS' table where the 'product_id' is present in the list. Assume 'malicious_product_ids' contains thousands of product IDs. Which of the following approaches is the MOST efficient and scalable for deleting these records using Snowpark?

A) Option A
B) Option E
C) Option D
D) Option B
E) Option C

5. You are tasked with building a Snowpark Python application to process JSON files stored in a Snowflake stage. The JSON files contain customer feedback data, including sentiment scores. You need to create a stored procedure that reads the JSON files, calculates the average sentiment score, and stores the result in a Snowflake table. You also need to handle potential errors, such as invalid JSON format in some files, and continue processing other files. Which of the following approaches is MOST efficient and robust to handle this scenario?

A) Download the JSON files to the Snowpark client, process them using standard Python libraries (like 'json'), and then upload the aggregated data back to Snowflake using 'session.write_pandas()'. Handle errors locally and log them.
B) Implement an external function using AWS Lambda or Azure Functions to parse the JSON files and calculate the average sentiment score. Call this external function from the stored procedure. This provides better error handling and scalability.
C) Use the function directly within the stored procedure to parse each JSON file. Catch exceptions within the loop and log errors to a separate table. Use 'DataFrame.write.mode('append')' to write the average sentiment score to the target table.
D) Use and return a DataFrame containing the average sentiment score and file name for each processed file. Handle JSON parsing errors by skipping the file and logging the error. Use to write the DataFrame to the target table.
E) Load the JSON data into a VARIANT column in a temporary table. Use a Snowpark DataFrame transformation to parse the JSON data from the VARIANT column. Catch errors during the DataFrame transformation process and log them to a separate table. Finally, calculate the average sentiment score using Snowpark functions.

Solutions: