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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. 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) Calculate the maximum salary per department, then filter employees whose salary is greater than or equal to 80% of the maximum salary.
B) 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.
C) 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.
D) Calculate the average salary per department, then filter employees whose salary is greater than 80% of the average salary.
E) 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.

2. You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?

A)

B)

C)

D)

E)

3. You have a Pandas DataFrame named containing employee information including 'name' , 'department, and You want to create a Snowpark DataFrame named from this Pandas DataFrame and register it as a temporary view named 'TEMP EMPLOYEES. However, you need to ensure that any NULL values in the Pandas DataFrame are handled correctly when creating the Snowpark DataFrame. Which of the following code snippets achieves this, minimizes data transfer and provides best performance considering dataset size is large?

A)

B)

C)

D)

E)

4. Consider the following Snowflake SQL statement intended to modify the properties of a Snowpark-optimized virtual warehouse named
'SNOWPARK_WH':

Which of the following statements accurately describe the expected outcome of executing this SQL statement?

A) The SQL statement will execute successfully, resizing the 'SNOWPARK WI-i' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
B) The SQL statement will fail because the 'SCALING POLICY parameter cannot be set for Snowpark-optimized warehouses.
C) The SQL statement will execute successfully only if the user executing it has the 'MODIFY privilege on the 'SNOWPARK_WH' warehouse.
D) The SQL statement will execute successfully after checking if 'SNOWPARK_WH' is of Snowpark-optimized warehouse type, resizing the 'SNOWPARK_WH' warehouse to 'LARGE', setting the maximum number of clusters to 3, the minimum to 1, and enabling the 'ECONOMY' scaling policy.
E) The SQL statement will fail because you cannot modify 'WAREHOUSE_SIZE and 'MAX CLUSTER COUNT in a single SALTER WAREHOUSE statement.

5. You are tasked with creating a Snowpark DataFrame from a Python list of tuples. Each tuple represents a customer record with the following structure: '(customer_id, signup_date, The 'customer _ id' should be an integer, 'signup_date' should be a date, and should be a decimal. You want to define the schema explicitly for type safety and performance. Which of the following code snippets correctly defines the schema and creates the Snowpark DataFrame?

A)

B)

C)

D)

E)