Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You're tasked with creating a Snowpark UDF to calculate the Haversine distance between two sets of latitude and longitude coordinates (point A and point B). Which of the following statements about deploying and using this UDF is/are TRUE?

A) The UDF can only be called directly from within the Snowpark session and cannot be used in standard Snowflake SQL queries.
B) The UDF can be written in Python, Java, or Scala. Using a Java UDF will likely offer best performance, especially when dealing with very large datasets. You'll need to stage the compiled JAR file on an internal stage that Snowpark can access.
C) When defining the UDF with input types, the Python types must exactly match the corresponding Snowflake data types.
D) The UDF can only be written in Python and must be deployed as an inline UDF within the Snowpark session.
E) The UDF, once defined, can be used inside of any DataFrame operation like 'select', 'filter' , and 'withColumn'

2. You are developing a Snowpark application to analyze website traffic data'. You have a DataFrame named 'website_logs' with columns 'user_id', 'page_url', and 'timestamp'. You need to create a new DataFrame that contains the count of distinct users who visited each page within a specific time window Consider the following (incomplete) Snowpark Python code:

Which of the following code lines, when inserted into the Complete the following line...' comment, will correctly calculate the approximate distinct user count for each page within the specified time window?

A) website_logs.with_column('distinct_users', F.approx_count_distinct('user_id').over(window_spec))
B) website_logs.groupBy('page_url').agg(F.countDistinct('user_id').alias('distinct_users'))
C) _logs.with_column('distinct_users', F.countDistinct('user_id').over(window_spec)) website
D) website_logs.with_column('distinct_users', F.count('user_id').over(window_spec))
E) website_logs.groupBy('page_url', F.window('timestamp', '1 hour')).agg(F.countDistinct('user_id').alias('distinct_users'))

3. Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A) The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
B) The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
C) The code establishes a connection to Snowflake using the provided credentials.
D) The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
E) The code reads data from a table named 'my_table' in Snowflake.

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

5. You are tasked with creating a UDTF using Snowpark Python that splits a comma-separated string of customer IDs into individual rows. The input is a string (VARCHAR) and the output should be a table with a single column named 'customer_id' of type INTEGER. Which of the following code snippets CORRECTLY defines and registers the UDTF, ensuring proper data type handling? Assume is a valid Snowpark Session object.

A)

B)

C)

D)

E)

Solutions: