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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are developing a Snowpark application in Python to process financial transactions. You're using a Snowpark DataFrame named 'transactions_df which contains sensitive financial data, including customer account numbers and transaction amounts. To comply with data privacy regulations, you need to mask the customer account numbers before performing any aggregations. The 'transactions_df DataFrame has a column named 'ACCOUNT NUMBER' (VARCHAR). You decide to use a User-Defined Function (UDF) to mask the account numbers using a cryptographic hashing algorithm. Which of the following approaches is the most secure and efficient way to define and use the UDF in Snowpark, ensuring the masking occurs within the Snowflake environment and minimizes the risk of exposing sensitive data?

A) Create a Java UDF that uses a strong cryptographic hashing algorithm provided by the Java Cryptography Extension (JCE) to mask the account numbers. Deploy this UDF to Snowflake and apply it to the 'ACCOUNT_NUMBER column using 'transactions_df.select()'. Ensure the Java UDF has necessary dependencies available.
B) Create a Python UDF that retrieves the account numbers, performs the masking operation using Python's 'hashlib' library, and returns the masked account numbers as a new DataFrame. Apply this UDF to the 'ACCOUNT_NUMBER column using 'transactions_df.select()&.
C) Create a SQL UDF that uses Snowflake's built-in ' SHA2 function to mask the account numbers. Apply this UDF to the 'ACCOUNT_NUMBER column using 'transactions_df.select()'.
D) Extract the 'ACCOUNT NUMBER column from the 'transactions_df DataFrame, iterate through the account numbers in your Python application, mask them using a strong cryptographic algorithm, and then create a new Snowpark DataFrame with the masked account numbers.
E) Create a Python UDF that uses a simple string manipulation technique (e.g., replacing characters with asterisks) to mask the account numbers. Apply this UDF to the 'ACCOUNT_NUMBER column using 'transactions_df.select()'.

2. You are troubleshooting a Snowpark application that fails to connect to Snowflake intermittently. The error message indicates an issue with the specified account identifier Which of the following actions could help resolve this issue? Select all that apply.

A) Check the Snowflake network policy to ensure that the IP address from which the Snowpark application is connecting is allowed.
B) Confirm that the Snowflake service is not experiencing any outages in the specified region.
C) Ensure that the user specified in the connection parameters has the necessary privileges to access the database and schema.
D) Restart the Snowpark application server to clear any cached connection information.
E) Verify that the account identifier is correctly specified in the connection parameters, including the region if applicable.

3. You are developing a Snowpark stored procedure to perform sentiment analysis on customer reviews. You need to use the 'nltk' Python package, which is not a built-in package in Snowflake. You have already created a stage named 'my_stage' in Snowflake and uploaded the necessary nltk data files (e.g., 'vader_lexicon.zip') to the stage. Which of the following code snippets correctly configures the session and imports the required nltk components within the stored procedure?

A)

B)

C)

D)

E)

4. You are tasked with optimizing a Snowpark application that performs sentiment analysis on customer reviews using a Python UDE The UDF uses a large pre-trained natural language processing (NLP) model stored in a file named 'sentiment_model.pkl'. The current implementation loads the model from the stage for each row of data processed, which is impacting performance. How can you optimize the application to load the model only once per worker process?

A) Define 'sentiment_model.pkl' as a parameter during UDF definition to load only once per worker process and send it to the UDF.
B) Use to import 'sentiment_model.pkl'. Use the decorator from the 'functools' module to cache the model loading function, initializing the model outside of the UDF definition.
C) Implement a custom initialization function that loads the model and is called only once per worker process. Utilize the to retrieve and cache model during session initialization. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
D) Use a global variable to store the loaded model. Load the model from the stage into the global variable only if it is currently None. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.
E) Use the decorator from the 'functools' module to cache the model loading function. Upload 'sentiment_model.pkl' to a stage and reference it in the 'imports' clause.

5. You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?

A)

B)

C)

D)

E)

Solutions: