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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q155-Q160):

NEW QUESTION # 155
You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

A. Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.
B. Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
C. Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
D. Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.
E. Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.

Answer: A

Explanation:
Option D, using 'LATERAL FLATTEN' within a SQL context, is the most efficient approach. 'LATERAL FLATTEN' is designed specifically for flattening arrays in Snowflake and can handle nested structures efficiently within SQL. By crafting a SQL statement and using session.sqr, one can leverage the power of Snowflake's SQL engine for this task. Other options involve more complex code (UDFs, RDD conversions) or are less efficient (iterative exploding).

NEW QUESTION # 156
You are developing a Snowpark application that requires calling a stored procedure. Which of the following approaches is the MOST secure and efficient way to call a stored procedure from your Snowpark Python code, assuming the stored procedure returns a single value?

Answer: A

Explanation:
The 'session.call()' method (B) is the most direct and efficient way to call a stored procedure and retrieve its single return value from Snowpark Python. It handles the execution and data retrieval efficiently. Using 'session.sql()' (A and C) is less efficient as it requires parsing SQL and manual extraction of the result. 'session.sproc()' (D) is generally used to register Python functions as stored procedures and not for calling existing ones. is not valid, 'session.call()' is the right way to call procedure

NEW QUESTION # 157
You have a Snowpark DataFrame named 'products_df' with columns 'product_id' (INT), 'product_name' (VARCHAR), and 'price' (FLOAT). You want to create a new DataFrame called 'discounted_products df that includes all columns from 'products_df' plus a new column named 'discounted_price', which is calculated as the original price minus a discount percentage specified by the variable 'discount_rate' (e.g., 0.1 for 10%). The 'discount_rate' is stored in the database table named 'discount_table'. You want to load the rate to variable. Choose the correct ways to achieve this. (Select all that apply)

Answer: C,D,E

Explanation:
Options A, B and D provide valid ways to fetch 'discount_rate' as a single numerical value. And fetch the data and gets the first value from the first row. Similarly, gets the data and return the first row. However, Option C does not have LIMIT 1 and will not work. Option E fetches one row as one array, thus requires rate[0] to compute discounted_price.

NEW QUESTION # 158
A data engineering team wants to deploy a Snowpark Python stored procedure that aggregates sales data from a table 'SALES DATA and writes the results to a table 'AGGREGATED SALES. The stored procedure needs to be executed by various users with different roles. The team wants to ensure that users can only execute the stored procedure and cannot directly access the underlying 'SALES DATA' table. Which approach is most suitable for managing data access and security in this scenario, and what are the implications of using 'EXECUTE AS OWNER vs 'EXECUTE AS CALLER?

A. Create the stored procedure with 'EXECUTE AS CALLER and grant 'USAGE on the database and schema. The callers must have access to both the AGGREGATED SALES and SALES DATA tables. The stored procedure will use the caller's privileges for all operations.
B. Create a view on the 'SALES_DATX table that only exposes the necessary columns and grant 'SELECT privilege on the view to the roles that need to execute the stored procedure. Create the stored procedure with EXECUTE AS CALLER to leverage the view's column restrictions.
C. Create the stored procedure with 'EXECUTE AS OWNER and grant 'USAGE privilege on the database and schema containing the stored procedure to the roles that need to execute it. This hides the underlying table from the caller, and the stored procedure executes with the owner's privileges.
D. Create the stored procedure with 'EXECUTE AS OWNER , grant 'USAGE on the database and schema. Grant 'EXECUTE TASK' on the stored procedure to the specific roles, while the Owner(Role with Execute Task permission) should have access to SALES DATA table.
E. Create the stored procedure with 'EXECUTE AS CALLER and grant 'SELECT privilege on the 'SALES DATA' table to all roles that need to execute the stored procedure. This allows the stored procedure to execute with the caller's privileges, and the caller must have the necessary privileges to access the underlying tables.

Answer: C

Explanation:
'EXECUTE AS OWNER allows the stored procedure to run with the privileges of the stored procedure's owner, effectively hiding the underlying tables from the caller. This is crucial for security. 'EXECUTE AS CALLER requires the caller to have the necessary privileges on the underlying tables, which defeats the purpose of restricting direct access. While creating a view is an option, it adds complexity and may not be necessary if 'EXECUTE AS OWNER is used correctly. Option D mentions 'EXECUTE TASK which isn't the right privilege needed for stored procedures execution. 'EXECUTE AS CALLER in Option E opens up direct access to underlying tables.

NEW QUESTION # 159
You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?

A. Iterate through the rows of 'data_df , constructing and executing a separate INSERT statement for each row using 'session.sql()'.
B. Directly use the method, without any intermediate transformations.
C. Convert 'data_df to a Pandas DataFrame using and then use to insert the data.
D. Stage 'data_df to an internal stage, then load the data from stage to the table using COPY INTO command.
E. Load the DataFrame into chunks of 1000 records into target table by using insert_into() function iteratively

Answer: B

Explanation:
The most efficient approach is option C: 'data_df.insert_into('TARGET_TABLE')'. This method leverages Snowpark's optimized data transfer mechanisms and performs bulk insertion using the underlying Snowflake engine which minimizes api calls and faster to insert the data. Option A is highly inefficient due to the overhead of constructing and executing individual SQL statements. Option B introduces Pandas, which is slower. Option D involves additional staging steps. Option E is manual chunking which is also slower.

NEW QUESTION # 160
......

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