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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with creating a Snowpark DataFrame from a complex JSON structure stored in a VARIANT column named 'payload' within a table called 'events'. The 'payload' contains nested objects and arrays, and you need to extract specific fields into separate columns of the DataFrame. You need to extract the 'event_id' (INT) from the top level of the JSON, the 'user _ id' (INT) from the 'user' object nested within the 'payload' , and the first element of the 'tags' array (VARCHAR) also nested within the 'payload'. Which of the following code snippets correctly defines the schema using 'StructType' and 'StructField' and applies it during DataFrame creation assuming events table contains multiple rows?

A)

B)

C)

D)

E)

2. A data engineering team is developing a Snowpark stored procedure to perform complex data transformations and load the results into a target table. They want to operationalize this procedure by scheduling it to run daily. Which of the following is the MOST reliable and scalable way to schedule the execution of this Snowpark stored procedure within Snowflake?

A) Implement a Streamlit application that calls the stored procedure when a button is pressed.
B) Utilize a third-party orchestration tool, such as Airflow, to schedule and monitor the execution of the stored procedure through the Snowflake connector.
C) Use Snowflake Tasks to schedule a SQL statement that calls the stored procedure.
D) Create a Python script that uses the Snowpark API to connect to Snowflake and execute the stored procedure, then schedule the script using a Linux cron job.
E) Use Snowflake Pipes to ingest data and trigger the stored procedure based on new data arrival.

3. A data engineering team is migrating a series of complex SQL queries into Snowpark Python to leverage vectorized UDFs and optimize performance. They currently use several Common Table Expressions (CTEs) within their SQL queries. What is the most efficient and Pythonic approach to create a Snowpark DataFrame representing the result of a complex SQL query with multiple CTEs, minimizing code redundancy and maintaining readability?

A) Concatenate the SQL statements representing each CTE and the final SELECT statement into a single long string, then use to create the DataFrame.
B) Use the method to create separate Snowpark DataFrames for each CTE and then use Snowpark DataFrame joins to combine them into the final DataFrame.
C) Create separate temporary tables in Snowflake for each CTE using SQL, then create Snowpark DataFrames from these temporary tables using session.table(table_name)'.
D) Utilize the method to create a single Snowpark DataFrame by executing the entire SQL query with CTEs. Then, use Snowpark's DataFrame API for further transformations if needed.
E) Re-write all CTEs using Snowpark's DataFrame API directly, avoiding the use of 'session.sql()' altogether.

4. You are developing a Snowpark application to process customer sentiment from text reviews. You have a Python function, , that utilizes a pre-trained NLP model loaded from a file on a Snowflake stage named This function returns a sentiment score (float) between -1 and 1. You need to register this function as a UDF so that it can be used within Snowpark DataFrames. Which of the following code snippets correctly registers the UDF, ensuring the NLP model is available to the function during execution?

A)

B)

C)

D)

E)

5. You have a Snowflake table 'user_profiles' with a VARIANT column 'profile_data'. This column contains JSON objects, and one of the fields within these objects is an array called 'interests'. The 'interests' array contains JSON objects, each with 'name' and 'category' fields. You need to use Snowpark to flatten the 'interests' array and extract the 'name' and 'category' for all user profiles, but only for profiles where the user's 'status' is 'active'. You want to write this in the most efficient way possible. Which of the following code snippets will achieve this?

A)

B)

C)

D)

E)

Solutions: