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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are using Snowpark Python to analyze sales data stored in a Snowflake table named 'SALES DATA. The table has columns PRODUCT ICY, 'REGION', and 'SALE DATE. You need to calculate the total sale amount for each product in each region. You intend to use the 'group_by' and 'agg' functions. Which of the following Snowpark Python code snippets correctly performs this aggregation and renames the aggregated column to 'TOTAL SALES'? (Assume 'session' is a valid Snowpark session object.)

A)

B)

C)

D)

E)

2. You are using Snowpark Python to transform a large DataFrame containing customer transaction data'. You need to persist the resulting DataFrame as a new Snowflake table named 'CUSTOMER TRANSACTIONS AGGREGATED', replacing the existing table if it exists. You want to explicitly define the schema of the new table to ensure data types are correctly enforced. Which of the following code snippets achieves this most efficiently and correctly?

A)

B)

C)

D)

E)

3. You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

A)

B)

C)

D)

E)

4. You have a Snowpark Python application that reads data from a Snowflake table, performs a complex transformation using a User- Defined Table Function (UDTF), and then writes the transformed data back to a new Snowflake table. The UDTF is defined as follows:

You need to optimize the performance of this application. Which of the following strategies would be MOST effective in reducing the execution time of the UDTF?

A) Cache the input DataFrame before applying the UDTF using 'df.cache()'.
B) Reduce the number of rows in the input table by applying a filter before calling the UDTF.
C) Use a vectorized UDTF instead of a standard UDTF, processing data in batches. (Assume 'vectorized=True' is a valid parameter for 'udf .)
D) Increase the warehouse size to the largest available option before running the Snowpark application.
E) Replace the UDTF with a standard User-Defined Function (UDF) as UDFs are inherently faster.

5. You have a Snowpark DataFrame with columns 'product_id', 'customer_id', and 'sale_amount'. Some values are negative, indicating returns, and others are null. You need to replace negative values with 0 and fill null values with the average 'sale_amount' for each 'product_id'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark?

A)

B)

C)

D)

E)