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Answer: Use Azure Automation to track cluster performance, dynamically adjust sizes, and enforce security policies through scripts.
The most effective approach is **C**, which involves using Azure Automation to monitor cluster performance and dynamically adjust sizes while enforcing security policies via scripts. This method ensures that scaling adheres to organizational security policies efficiently and reliably. - **A** suggests manual adjustments, which are inefficient and error-prone. - **B** relies solely on Databricks runtime features, which may not suffice for dynamic scaling needs. - **D** automates scaling and applies predefined security settings, but lacks the flexibility to adapt to all organizational policies dynamically.
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How can you ensure that dynamic scaling of Azure Databricks clusters adheres to organizational security policies while handling variable workloads?
A
Manually adjust cluster sizes based on demand and apply security configurations each time.
B
Depend only on Databricks runtime security features to maintain security standards across all nodes without extra setup.
C
Use Azure Automation to track cluster performance, dynamically adjust sizes, and enforce security policies through scripts.
D
Set up auto-scaling policies in Databricks to automatically apply predefined security settings to new nodes.