What must you create in an Azure Databricks workspace to process data using code in notebooks?

To process data using code in notebooks within an Azure Databricks workspace, it is essential to create a Spark cluster. Azure Databricks is built on top of Apache Spark, which is a powerful distributed computing framework designed for big data processing.

When you create a Spark cluster in Databricks, it provides the computational resources required to execute code in notebooks. This cluster allows you to leverage Spark's capabilities for processing large datasets efficiently. By using Spark, users can run a variety of workloads, such as batch processing, streaming data processing, machine learning tasks, and more, using a collaborative notebook environment.

Other options, such as creating a SQL Warehouse or a Windows Server virtual machine, do not offer the necessary distributed processing capabilities inherent to Spark. Additionally, while a Data Lake Storage account is important for storing data, it does not contribute to the execution of code within Databricks notebooks. Therefore, creating a Spark cluster is crucial for effective data processing in Azure Databricks.