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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
B) The 'countl' operation will trigger the materialization and caching of 'filtered_df.
C) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
D) The 'filter' operation Ccol('column_a') > 100') will be executed twice.
E) The 'count? operation will use the cached results of and apply an additional filter on the cached data.

2. You are developing a secure UDF in Snowpark Python that needs to access sensitive data stored in an internal stage. The UDF should be accessible to users without granting them direct access to the stage. Which of the following security measures and code snippets are required to achieve this, assuming the stage is already created?

A) Create a secure UDF using the ' VOLATILE keyword, allowing it to access secured data with current user's permissions.
B) Create a secure UDF and use the function to access stage credentials within the UDF's handler function.
C) Create a secure UDF and use a stored procedure owned by a role with access to the internal stage to retrieve data, passing the data to the UDF as an argument.
D) Create an external function and grant access to the API integration that provides the security context.
E) Create a UDF and grant USAGE on the stage to the role that owns the UDF.

3. You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

A) Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
B) Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
C) Only setting the 'role' parameter to a role with appropriate privileges.
D) Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
E) Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.

4. You are developing a Snowpark application that processes real-time streaming data'. The application needs to perform a complex calculation for each incoming event. To improve performance, you decide to leverage asynchronous execution and User-Defined Functions (UDFs). However, you are encountering issues with the order of results and ensuring that the processing order matches the arrival order of the events. Which of the following strategies MOST effectively addresses the challenge of maintaining processing order while leveraging asynchronous execution and UDFs in Snowpark?

A) Employ asynchronous UDF calls with 'block-False' and rely on Snowflake's internal optimization to maintain the processing order.
B) Utilize asynchronous UDF calls with 'block-False' and implement a custom ordering mechanism based on a timestamp or sequence number associated with each event. Store the results in a temporary table and sort them based on the timestamp before further processing.
C) Use synchronous UDF calls with a large Snowflake warehouse to minimize processing time and guarantee order.
D) Abandon the use of UDFs altogether and reimplement the complex calculation using only built-in Snowpark DataFrame transformations to ensure order.
E) Use synchronous UDF calls with a small Snowflake warehouse to introduce artificial delays and ensure order.

5. You are working with Snowpark to create a DataFrame from a Python dictionary where keys represent column names and values are lists representing column data'. However, the dictionary contains lists of varying lengths for different columns. You need to create a DataFrame from the Python dictionary but are unsure how to create it. Which approach should you take and why?

A) DataFrame to a Snowpark DataFrame using 'session.createDataFrame(pandas_df)'. Snowpark does not support creating DataFrames directly from dictionaries with lists of varying lengths. The code will throw an error. So, manually build the logic of combining the lists.
B) Create a Pandas DataFrame from the dictionary first. Pandas handles lists of unequal lengths by filling the shorter lists with NaN. Then, convert the Pandas
C) Transform the dictionary into a list of dictionaries or tuples, padding the short lists with 'None' values. Then, define a schema and use 'session.createDataFrame(data, schema=schema)' to create the DataFrame.
D) Attempt to create the DataFrame directly using 'session.createDataFrame(data)'. Snowpark will automatically pad the shorter lists with 'NULL' values to match the length of the longest list.
E) Manually pad all lists in the dictionary with 'None' values until they have the same length. Then, create the DataFrame using 'session.createDataFrame(data)'.

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