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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowflake table 'raw_events' containing JSON data in a VARIANT column named 'event_data'. This column contains nested JSON objects representing user activity on a website. You need to extract specific nested values and load them into a new Snowflake table 'user_activity' with columns 'event_type' , and 'timestamp'. Which of the following Snowpark code snippets is the MOST efficient and correct way to achieve this, assuming you want to minimize data transfer and optimize performance? Consider the potential for null values within the JSON.

A)

B)

C)

D)

E)

2. You have a Snowpark DataFrame with columns 'sale_date', 'product_id', and 'revenue'. You need to calculate the cumulative revenue for each product over time. Which of the following approaches will accomplish this in Snowpark using window functions?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame named 'employee_df in VS Code. You want to define a user-defined function (UDF) in Python that calculates the bonus for each employee based on their salary and performance rating. The UDF should take the salary (SALARY) and performance rating (PERFORMANCE RATING) as inputs and return the bonus amount. Assume you have already established a Snowpark session. Which code snippet accurately defines and registers a UDF named 'calculate_bonus' that can be applied to the 'employee_df DataFrame?

A)

B)

C)

D)

E)

5. You have a Pandas DataFrame 'products df containing product information that you want to upload to a Snowflake table named 'PRODUCTS' within the schema 'SALES'. The table might or might not exist. If it doesn't, you want to create it automatically. The products df DataFrame has a column 'product_name' that contains Unicode characters. Furthermore, you need to specify the column types directly as part of the operation. Which of the following options provides the most appropriate approach to achieve this with Snowpark?

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B)

C)

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Solutions: