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Answer: Develop a heuristic to label historical data, then train a model to predict anomalies from this labeled dataset.
The most efficient and scalable approach is to first develop a simple heuristic to label the unlabeled historical performance data. This labeled dataset can then be used to train a predictive model for anomalies. This method avoids the time and expense of manual labeling and is more efficient for predictive purposes. Option A is not scalable or cost-effective due to the manual effort required. Option B lacks the predictive capability needed for maintenance. Option D does not address the initial need for labeled data to train the model effectively.
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Your team is developing a predictive maintenance solution for server failures using unlabeled monitoring data from a large data center. The solution must be scalable, cost-effective, and quickly implementable to prevent potential server downtimes. Given these constraints, what is the BEST first step to take? Choose one correct option.
A
Hire a team of qualified analysts to manually label the historical performance data, then train a model on this dataset.
B
Create a simple heuristic, like a z-score, to label historical performance data and use this for real-time monitoring.
C
Develop a heuristic to label historical data, then train a model to predict anomalies from this labeled dataset.
D
Train a time-series model to predict performance values and alert on significant deviations from predictions.
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