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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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 environment variables and retrieving them using 'os.environ' to build the connection parameters. This is a secure and recommended approach.
B) 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.
C) Storing credentials in a Snowflake stage and retrieving them from there at runtime. This is an acceptable, though more complex, solution.
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) 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.

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

3. You are developing a Snowpark stored procedure in Python to perform sentiment analysis on customer reviews. The procedure relies on a custom Python library, 'sentiment_analyzer.py' , which is not available in Snowflake's default Anaconda channel. You also need to include the 'nltk' library. Which of the following approaches is the MOST efficient and recommended way to make both dependencies available to your stored procedure within Snowflake?

A) Include the code from 'sentiment_analyzer.py' directly within the stored procedure's Python code and download 'nltk' modules from the internet each time the stored procedure is executed.
B) Create a Snowflake Anaconda channel package containing 'sentiment_analyzer.pV and 'nltk' using 'conda build' , then reference this package in your stored procedure's 'imports' parameter.
C) Create a ZIP file containing 'sentiment_analyzer.py' and the required 'nltk' modules, upload it to a stage, and specify the stage path in the 'imports' parameter of the 'sproc' decorator.
D) Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
E) Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.

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

5. You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

A) Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.
B) Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.
C) Implement caching for the UDF's results to avoid recomputing the same values multiple times.
D) Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.
E) Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.

Solutions: