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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?

A)

B)

C)

D)

E)

2. Consider the following scenario: You need to implement a UDF in Snowpark Python to calculate the distance between two geographical coordinates (latitude and longitude). The UDF should handle potential null values gracefully and return null if either input coordinate is null. Which code snippet demonstrates the MOST efficient and correct implementation, leveraging Snowpark's capabilities?

A)

B)

C)

D)

E)

3. You have a Snowpark DataFrame 'df containing customer data with columns 'customer_id', 'signup_date' (TIMESTAMP NTZ), and 'country'. You need to create a new DataFrame that calculates the number of days since each customer signed up, but only for customers in 'USA' and 'Canada'. Furthermore, you want to filter out records where the signup was more than 365 days ago. Which of the following Snowpark code snippets will achieve this most efficiently?

A)

B)

C)

D)

E)

4. You have a Snowpark application that processes sensor data from a large number of devices. You've implemented a scalar UDF in Python to calculate a complex statistical metric for each sensor reading. Initial tests show poor performance. Which of the following strategies would be MOST effective in improving the performance of this application, considering the nature of the computation and the data volume?

A) Replace the Python scalar UDF with a Python vectorized UDF, ensuring the UDF processes data in batches instead of row-by-row.
B) Increase the warehouse size significantly to provide more resources for the scalar UDF execution.
C) Replace the Python scalar UDF with a Java scalar UDF, as Java typically offers better performance than Python in Snowflake.
D) Implement caching for the UDF results, assuming the statistical metric calculations are frequently repeated for the same sensor readings.
E) Rewrite the UDF logic directly in SQL, leveraging Snowflake's built-in functions for statistical calculations.

5. You are building a Snowpark application that involves a UDF. Consider that you are creating UDF as follows:

A) There will be no attempt to register UDF until .collect()' action is performed.
B) The UDF is registered lazily; only when called the first time will it be registered and available in the session.
C) UDF registration happens on the client side, before dataframe transformations are evaluated and sent to Snowflake.
D) UDF registration occurs on the server-side, only when a dataframe action such as collect or show is called .
E) The behaviour of the UDF registration dependends on the size of code in it.

Solutions: