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Answer: Utilizing Azure Databricks' autoscaling feature with synthetic data that simulates expected loads during the test
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. - **Option A** involves extrapolating from small-scale tests, which may not accurately reflect full load conditions. - **Option B** relies on manual adjustments based on current workloads, which may not account for the variability and scale of the upcoming launch. - **Option D** is reactive rather than proactive, risking performance issues during the critical launch period. 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.
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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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