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

2. A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?

A) Run the stored procedure on a larger Snowflake warehouse to reduce execution time, regardless of potential idle time.
B) Specify a warehouse size using the 'warehouse' parameter within the '@sproc' decorator and leverage auto-suspend and auto-resume features to minimize costs when the procedure is idle.
C) Split the stored procedure into multiple smaller procedures and execute them sequentially on a smaller warehouse.
D) Convert the Python stored procedure to a SQL stored procedure to leverage Snowflake's SQL optimization engine.
E) Register the stored procedure with the '@sproc' decorator without specifying any warehouse size, letting Snowflake automatically manage the warehouse.

3. You have two Snowflake tables, 'customers' and 'orders'. The 'customers' table contains customer information, including a 'customer id' and 'region'. The 'orders' table contains order information, including 'order id', 'customer id', and 'order amount'. You need to create a Snowpark DataFrame that joins these two tables on 'customer id' and calculates the total order amount per region. However, some customers may not have any orders, and you want to include all customers in the result, with a total order amount of 0 for those without orders. Which of the following Snowpark code snippets will achieve this goal MOST efficiently, assuming 'customers_df and 'orders_ff are pre-existing Snowpark DataFrames representing the respective tables?

A)

B)

C)

D)

E)

4. You are developing a Snowpark application to process large datasets stored in Snowflake. You need to create a session using the 'snowflake.connector.connect' method. Which of the following code snippets correctly establishes a session with Snowflake, leveraging an external browser authentication mechanism, ensuring secure and reliable access to your data while minimizing exposed credentials in the code?

A)

B)

C)

D)

E)

5. You are working with a Snowpark DataFrame 'transactions df that contains customer transaction data'. This data includes a 'transaction amount' column and a 'transaction date' column. You need to create a new feature called 'is weekend transaction' that indicates whether a transaction occurred on a weekend (Saturday or Sunday). Furthermore, some 'transaction_date' values are missing. You want to impute the missing dates with the mode (most frequent date) before determining if the transaction occurred on a weekend. Which of the following steps, when combined, provide the correct and most efficient approach to achieve this?

A) 1. Replace the null values in 'transaction_date' column with a constant string like '1900-01-01'.2. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 3. Apply the UDF to the 'transaction_dates column to create the column. 4. After applying the UDF convert back the replaced values in transaction_date to null.
B) 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 3. Create a UDF using datetime library that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the column.
C) 1. Calculate the mode of the 'transaction_date' column using Snowpark functions. 2. Fill the missing values in the 'transaction_date' column with the calculated mode using 'fillna()'. 3. Use the 'dayofweek' function to determine the day of the week and create using a 'when' condition.
D) 1. Calculate the mode of the 'transaction_date' column. 2. Fill the missing values in the 'transaction_date' column with the calculated mode. 3. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 4. Apply the UDF to the 'transaction_date' column to create the 'is weekend transaction' column.
E) 1. Calculate the mode of the 'transaction_date' column. 2. Filter all rows where 'transaction_date' is null and load that data into a temporary table. 3. Update all rows in original 'transactions_df from temporary table. 4. Create a UDF that takes a date as input and returns True if it's a weekend (Saturday or Sunday), False otherwise. 5. Apply the UDF to the 'transaction_date' column to create the column.

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