Databricks Certified Data Engineer - Associate

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You are working with a Spark SQL table df_employees that contains the following schema:

employee_id ARRAY<ARRAY<INT>>,
name STRING,
department STRING,
salary DOUBLE

employee_id ARRAY<ARRAY<INT>>,
name STRING,
department STRING,
salary DOUBLE

Each row in employee_id contains multiple arrays of employee IDs (for example: [[101,102],[103,104]]).

Your task is to convert the employee_id column into a single array of integers per row, without increasing the number of rows, using Spark SQL.

Which of the following queries correctly accomplishes this task?

Simulated

Last updated: February 18, 2026 at 02:33

SELECT flatten(employee_id) as employee_ids FROM df_employees

SELECT flatten(employee_id) as employee_ids FROM df_employees

52.5%

Comments

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SELECT explode(employee_id) AS employee_id
FROM df_employees

SELECT explode(employee_id) AS employee_id
FROM df_employees

15.3%

SELECT explode(flatten(employee_id)) AS employee_id
FROM df_employees

SELECT explode(flatten(employee_id)) AS employee_id
FROM df_employees

11.5%

SELECT employee_id
FROM df_employees
LATERAL VIEW explode(employee_id) e AS employee_id

SELECT employee_id
FROM df_employees
LATERAL VIEW explode(employee_id) e AS employee_id

18.1%