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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:
1. You have a Snowpark DataFrame 'employees' with columns 'employee_id' (INT), 'name' (STRING), 'department' (STRING), and 'salary' (DOUBLE). You want to create a new DataFrame that contains the top 3 highest-paid employees within each department. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?
A)
B)
C)
D)
E)

2. A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?
A) GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
B) GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
C) GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,
D) GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
E) GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
3. You are developing a Snowpark Python stored procedure that needs to interact with an external REST API. The API requires authentication using an API key, which you want to store securely and access within the stored procedure. What is the MOST secure and recommended way to store and retrieve the API key within the stored procedure?
A) Store the API key in a Snowflake table and query it within the stored procedure.
B) Store the API key as an environment variable within the Snowflake warehouse configuration.
C) Store the API key as a constant string within the stored procedure's code.
D) Store the API key in a Snowflake Secret and access it using the 'secrets' module within the stored procedure.
E) Store the API Key as a comment in the Store procedure code, and retrieve it using REGEX
4. You are working with a Snowpark DataFrame 'df that contains user profile data'. A column named 'profile' stores user information as JSON, including 'age' (which can be a number or a string), 'is active' (which can be a boolean or a string 'true'/'false'), and registration date' (stored as a string in 'YYYY-MM-DD' format). You need to perform the following data transformations: 1. Cast the 'age' to an integer, defaulting to -1 if casting fails. 2. Cast 'is active' to a boolean, treating 'true' (case-insensitive) as true and any other string as false. 3. Convert 'registration_date' to a date object. Select the code snippets (multiple answers can be correct) that correctly accomplish these tasks using Snowpark DataFrame transformations.
A)
B)
C)
D)
E)

5. You're working with Snowpark and have a DataFrame 'df containing a column 'json_data' with JSON strings. Some of these JSON strings are invalid. You need to parse the valid JSON strings and extract a field named 'product_id' from them. Invalid JSON strings should result in a 'NULL' value for the extracted 'product_id'. Which of the following approaches is the MOST robust and efficient way to achieve this?
A)
B)
C)
D)
E)

Solutions:
Question # 1 Answer: B,C,E | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: B,C | Question # 5 Answer: D |