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Answer: Create a new cluster, select the Data Engineering runtime, enable Unity Catalog, and configure auto-scaling to optimize costs.
The BEST approach to creating a UC-enabled all-purpose cluster in Azure Databricks for a project requiring data governance, scalability, and cost efficiency involves selecting the Data Engineering runtime, which is specifically designed for data engineering tasks and supports Unity Catalog for data governance. Enabling auto-scaling allows the cluster to dynamically adjust resources based on workload, optimizing costs without sacrificing performance. The other options either select runtimes not ideally suited for data engineering tasks or propose cost-saving measures that may compromise performance or governance capabilities.
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As a Databricks Certified Data Engineer - Associate, you are tasked with setting up a UC-enabled all-purpose cluster in Azure Databricks for a project that requires strict data governance and compliance with GDPR. The project involves processing large datasets with varying workloads, and the solution must be cost-effective while ensuring high performance. Which of the following steps is the BEST approach to create such a cluster, considering the need for data governance, scalability, and cost efficiency? (Choose one option)
A
Create a new cluster, select the Data Engineering runtime, enable Unity Catalog, and configure auto-scaling to optimize costs.
B
Create a new cluster, select the Machine Learning runtime, enable Unity Catalog, and manually set the number of workers to control costs.
C
Create a new cluster, select the Data Science runtime, enable Unity Catalog, and use spot instances to reduce expenses.
D
Create a new cluster, select the Analytics runtime, enable Unity Catalog, and disable auto-scaling to maintain consistent performance.
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