Explanation:
The optimal approach is C. Utilizing Azure Databricks' autoscaling feature with synthetic data that simulates expected loads during the test. This method effectively prepares your clusters by automatically adjusting resources to meet the simulated demand, ensuring readiness for the actual launch.
By choosing Option C, you leverage Azure Databricks' capabilities to simulate and adapt to expected loads, minimizing risks and optimizing performance ahead of the launch.
Ultimate access to all questions.
How can you ensure your Azure Databricks clusters are prepared for an expected increase in load before a major launch?
A
Conducting a small-scale load test and predicting full load performance based on those results
B
Estimating cluster capacity from current workloads and manually adjusting resources
C
Utilizing Azure Databricks' autoscaling feature with synthetic data that simulates expected loads during the test
D
Monitoring and adjusting cluster size in real-time during the actual launch
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