DAA-C01 Exam Dumps | A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a

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Question 11/151

A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

A. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.

B. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.

C. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.

D. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.

E. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.

Correct Answer: A

Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.

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