The correct answer is D. Hiring a team of qualified analysts to review and label the machines’ historical performance data provides high-quality labeled data, which serves as the ground truth for training a predictive maintenance model. Manual labeling allows you to accurately identify instances of actual failures and non-failure states in the historical performance data. This leads to a more robust and reliable machine learning model as compared to using heuristics, which may not capture complex patterns associated with server failures. Heuristics can be quick to implement but they often lack the accuracy and comprehensiveness required for such critical tasks.