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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark application connects to Snowflake using key pair authentication. After several successful executions, the application starts failing with authentication errors. You suspect an issue with the private key. Considering best practices for security and troubleshooting, which of the following actions should you take FIRST to diagnose and resolve the problem?

A) Restart the Snowpark application server to refresh the session and clear any cached credentials.
B) Check the Snowflake login history in ACCOUNT _ USAGE view to identify the specific error message and the IP address from which the failed login attempts originated. Use new authtype and password.
C) Rotate the public/private key pair immediately and update the Snowpark application with the new private key. Generate new username and password.
D) Immediately revoke the user's access to Snowflake and create a new user with a different private key.
E) Verify that the private key file exists at the specified path in the Snowpark application configuration and that the user running the application has read permissions on the file.

2. You have a Snowpark DataFrame 'product_df and you want to create a view named 'active_products' that only includes products with a 'status' column value equal to 'ACTIVE'. This view should be accessible to all users in the 'PUBLIC' role. Which of the following code snippets correctly creates the view and grants the necessary privileges?

A)

B)

C)

D)

E)

3. You are building a Snowpark application that uses a Python UDF to perform sentiment analysis on customer reviews. The UDF relies on a large pre-trained machine learning model loaded from a file. During execution, you encounter 'Out of Memory' errors within the UDF. Considering the constraints of the Snowpark execution environment and the need to optimize resource usage, which of the following steps is the MOST effective in addressing this issue and ensuring the application's stability and performance?

A) Use Snowpark's 'sproc' to register the UDF as a stored procedure instead ofa UDF, as stored procedures typically have more memory allocated to them.
B) Increase the overall size of the Snowflake warehouse to provide more memory for UDF execution. The Snowflake environment will automatically allocate more memory to UDFs when available.
C) Implement lazy loading of the machine learning model within the UDF, ensuring that the model is loaded only when it's first needed, and then cached for subsequent calls within the same UDF invocation.
D) Optimize the model itself by reducing it's size through quantization or distillation, and re-upload the smaller model to the Snowflake stage for the UDF to use.
E) Break down the customer reviews into smaller chunks and process them in batches within the UDF, clearing the model from memory after each batch to reduce overall memory consumption.

4. You need to create a Snowpark DataFrame using a SQL query. The query requires a user-defined variable (e.g., a date for filtering records). What are the correct and recommended ways to safely pass this variable into the SQL query when creating the DataFrame using 'session.sql()' to prevent SQL injection vulnerabilities?

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame named 'transactions' containing transaction data'. You need to create a UDTF using Python to categorize transactions into 'High Value', 'Medium Value', and 'Low Value' based on the transaction amount and the customer's region. The categorization logic requires access to a dynamically updated lookup table stored in a Snowflake stage. Which approach would be MOST efficient and scalable, minimizing data transfer and maximizing Snowpark's vectorized operations?

A) Use a UDTF with the parameter, reading the lookup table directly into the UDTF using a Snowpark DataFrame and joining it with each batch of the 'transactions DataFrame. Materialize the result to a temporary table.
B) Create a vectorized UDF. Load the lookup table from the stage into the UDF's environment once during initialization. Then, process transactions in batches using pandas DataFrames within the UDF.
C) Use a scalar UDF, reading the lookup table from the stage for each transaction. This ensures data consistency but may incur significant overhead for each row processed.
D) Define a scalar UDF that queries the lookup table directly from Snowflake using a Snowflake connector. This avoids data transfer to the UDF but introduces external dependency and connection management overhead for each row.
E) Create a vectorized UDTF that loads the lookup table into memory during the first call, and then caches it for subsequent calls. Implement a refresh mechanism using a Snowflake external function triggered by stage updates.

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