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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are setting up a VS Code development environment for Snowpark with the Snowflake extension. You want to ensure that you can securely authenticate to Snowflake and execute Snowpark code. Which of the following steps are essential to configure secure authentication within VS Code for Snowpark?

A) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use MFA. Ensure the username and password is provided with a valid MFA token.
B) Install the Snowflake VS Code extension and configure the Snowflake connection settings in the extension's configuration file using username and password.
C) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Key Pair authentication. Ensure the private key is securely stored and referenced in the connection settings.
D) Install the Snowflake VS Code extension and configure the Snowflake connection settings to use Snowflake Native Authentication. Ensure that the user has the required permissions to authenticate using this method.
E) Install the Snowflake VS Code extension and configure the connection settings to use OAuth. Ensure the OAuth client and secret are properly configured in Snowflake and referenced in the connection settings.

2. A data science team wants to operationalize a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF, 'analyze sentiment(review_text)', is currently defined within a Snowpark session. Which of the following approaches is the MOST efficient and scalable way to deploy this UDF for real-time scoring of incoming review data in a Snowflake table named 'CUSTOMER REVIEWS'?

A) Persist the Snowpark session with UDF definition using pickle and call it from another Snowpark session.
B) Call the 'analyze_sentiment' UDF directly within a Snowpark DataFrame transformation that reads from 'CUSTOMER_REVIEWS'.
C) Register the 'analyze_sentiment' UDF as a persistent UDF in Snowflake and then call it from a SQL query that reads from 'CUSTOMER REVIEWS.
D) Package the 'analyze_sentiment' function as a stored procedure and execute it using Snowpark session.execute.
E) Create a Snowpark Dataframe that reads from 'CUSTOMER_REVIEWS , convert it to pandas dataframe and call analyze_sentiment function on pandas dataframe.

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) 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.
B) Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
C) 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.
D) 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.
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're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?

A) The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
B) The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'
C) When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.
D) The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.
E) The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.

5. You are using Snowpark in Python within a Jupyter Notebook environment to analyze sales data'. You've established a connection to Snowflake and loaded your data into a Snowpark DataFrame named 'sales ff. You need to calculate the cumulative sales for each product category over time. The 'sales_df' DataFrame has columns 'SALE DATE' (DATE), 'PRODUCT CATEGORY' (VARCHAR), and 'SALE AMOUNT' (NUMBER). Which of the following approaches, or combination of approaches, will correctly calculate the cumulative sales while optimizing for Snowflake's performance and scalability? (Select all that apply)

A) Use the and 'sort()' functions on the Snowpark DataFrame to order data by 'PRODUCT CATEGORY' and 'SALE DATE before calculating the cumulative sum using a UDF.
B) Use session.sql() to execute a SQL query with a window function for cumulative sales and load the data into snowpark DataFrame.
C) Use a Window function within a Snowpark DataFrame transformation to calculate the cumulative sum for each product category, ordered by 'SALE_DATE.
D) Write a stored procedure in Snowflake that calculates the cumulative sales and call the stored procedure from your Snowpark application.
E) Iterate through the rows of the 'sales_df DataFrame in the Jupyter Notebook and manually calculate the cumulative sales using Python code.

Solutions: