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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with creating a series of Snowpark DataFrames for a data transformation pipeline. For debugging purposes, you want to materialize these DataFrames as tables within Snowflake, but only for the duration of your session. You also need to make sure that these tables are automatically cleaned up when your session ends. Which of the following approaches offer(s) the MOST efficient and appropriate way to achieve this?

A) Persist each DataFrame using , and manually drop each table at the end of the session using 'session.sql(f'DROP TABLE {table_name}').collect()'.
B) Create each DataFrame as a local temporary view using and access these views via SQL within the same session.
C) Persist each DataFrame as a temporary table using , prepending a unique identifier to the table name to avoid naming conflicts.
D) Persist each DataFrame using 'df.write.mode('overwrite').option('temporary', 'true').save_as_table(table_namey.
E) Persist each DataFrame as a temporary table using using CTEs to perform the operations.

2. You have a Snowpark DataFrame with columns 'department' , and 'salary'. You want to identify employees in each department whose salary is within the top 20% of salaries for that department. Which of the following approaches, using window functions, is the MOST efficient way to achieve this?

A) Use the window function to calculate the percentile rank of each employee's salary within their department, then filter for ranks greater than or equal to 0.8.
B) Calculate the maximum salary per department, then filter employees whose salary is greater than or equal to 80% of the maximum salary.
C) Use window function to rank employees within each department by salary, then calculate the 80th percentile salary using a separate aggregation and join back to the original DataFrame to filter.
D) Use the 'ntile(5)' window function to divide each department's employees into 5 buckets based on salary, then select employees in the top bucket.
E) Calculate the average salary per department, then filter employees whose salary is greater than 80% of the average salary.

3. You have two Snowpark DataFrames: containing customer information and 'orders_df containing order details. You need to merge these DataFrames based on the column to create a unified view. The 'customers_df may contain duplicate values. The contains recent orders. You want to use 'merge' with an 'UPDATE action based on 'customer id' and a 'WHEN NOT MATCHED BY TARGET action to insert new customer records from 'customers df into the 'orders df table that do not exist.

A) The 'merge' operation will succeed, processing each matching record sequentially. If more than one row matches it will process based on order in the dataframe
B) The 'merge' operation will fail immediately upon encountering the first duplicate key during the update operation. Consider stage the source dataframe and use an appropriate dedup option with merge.
C) The 'UPDATE action in 'merge' will update all matching rows in the target table based on the join condition, regardless of duplicates in the source.
D) You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.
E) The merge operation cannot handle duplicate keys in the source DataFrame without pre-aggregation.

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) 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.
B) Use synchronous UDF calls with a large Snowflake warehouse to minimize processing time and guarantee order.
C) Abandon the use of UDFs altogether and reimplement the complex calculation using only built-in Snowpark DataFrame transformations to ensure order.
D) Employ asynchronous UDF calls with 'block-False' and rely on Snowflake's internal optimization to maintain the processing order.
E) Use synchronous UDF calls with a small Snowflake warehouse to introduce artificial delays and ensure order.

5. You have a Snowpark DataFrame 'df_orders' containing order data'. You want to delete all records from the underlying Snowflake table 'ORDERS TABLE' where the 'order_date' is older than '2023-01-01' using a Snowpark DataFrame operation. Which of the following code snippets is the MOST efficient and recommended way to achieve this, assuming 'spark' is your Snowpark Session object?

A) Option A
B) Option D
C) Option B
D) Option E
E) Option C

Solutions: