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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet that retrieves data and calculates aggregate values, however, the application performance is slow when you are fetching dataframe, given the 'block' parameter controls the synchronous/asynchronous behavior of the 'collect()' method. Choose ALL the statements about "session.create_dataframe([rowl ,row2],schema)' that are correct:

A) Consider replacing the call of 'collect()' with and converting Snowpark dataframe to Pandas dataframe. The dataframe operations could be efficient.
B) Increasing warehouse size of Snowflake will increase performance irrespective of the fact whether block is True or False.
C) The 'block' parameter has no influence on performance when creating dataframes, and hence the performance is mainly based on query optimization and ware house size.
D) The method, when executed with default 'block=True' , will wait for the result to be available before proceeding to the other statements in the code and hence performance is low. However, makes it asynchronous.
E) Snowflake warehouse size has no influence on dataframe creation, so if the dataframe is large and 'block=True' it will block the performance.

2. Consider the following Snowpark Python code snippet for creating a stored procedure:

What is the PRIMARY reason for explicitly defining 'input_types' and during the stored procedure registration?

A) To allow Snowflake to automatically generate documentation for the stored procedure's input and output types.
B) To enable the stored procedure to be called from other programming languages besides Python.
C) To ensure data type safety and schema validation during deployment and execution, preventing unexpected runtime errors due to type mismatches between the stored procedure and the calling environment.
D) To improve the performance of the stored procedure by enabling compile-time optimizations.
E) To allow Snowsight to correctly display the stored procedure's metadata, making it easier for users to understand its functionality.

3. You have a Python UDTF that calculates a running average from a stream of numerical data'. The UDTF's 'process' method maintains state (the running sum and count) between calls. You need to ensure that the UDTF's state is properly initialized for each new group of data processed within a Snowpark DataFrame. What are the requirements?

A) The UDTF class must define a '___del___' method. This method will be called by Snowpark at the beginning of processing each group of rows.
B) The UDTF class must have an '__init____' method to initialize the state variables. This '___init____' method will be called once per UDTF instance.
C) The UDTF class must define a 'end_partition' method to finalize processing and avoid memory leaks.
D) The UDTF class must define a '___init___' method to initialize the state variables and also 'reset method. This '___init____' and 'reset methods will be called once per UDTF instance.
E) The UDTF class must define a 'reset' method. This method will be called by Snowpark at the beginning of processing each group of rows.

4. You are tasked with building a Snowpark function to perform an upsert operation on a Snowflake table using a DataFrame. The function should take the target table name, a staging DataFrame, a join key column, and a list of columns to update. The function needs to handle potential schema evolution (i.e., columns may be added or removed from either the target table or the staging DataFrame) gracefully without causing the entire upsert to fail. Which of the following approaches, or combinations of approaches, would best address this requirement?

A) Rely on Snowflake's automatic schema detection during the 'merge' operation to automatically adapt to schema changes.
B) Before the 'merge' operation, use 'DataFrame.select' on the staging DataFrame to project only the columns that exist in the target table.
C) Before the merge, create a temporary table with the exact schema of the target table, insert all the data from the DataFrame into it, and then use the temporary table as source for the merge. Handle the schema evolution with dynamic sql if required.
D) Dynamically generate the SQL 'MERGE' statement within the function, comparing the columns present in the target table and the staging DataFrame, and only including those columns that exist in both.
E) Use the 'exceptAll' to ensure that there are no schema evolution issues.

5. You have a Snowflake table containing JSON data with nested arrays and objects representing website user interactions. You want to extract all 'product_id' values from within an array named 'viewed _ products' nested inside a 'session' object for each event, using Snowpark for Python. Assume the 'raw_events' table has a variant column called 'event_data". Which of the following Snowpark code snippets will correctly extract and flatten the 'product_id' values into a DataFrame?

A)

B)

C)

D)

E)

Solutions: