Answer-first summary for fast verification
Answer: Hire a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.
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.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
You are part of a team responsible for server maintenance in a large data center. Your management is requesting a predictive maintenance solution to detect potential server failures using monitoring data. However, the incident data has not been labeled yet, making it challenging to train a machine learning model immediately. What should be your first step in this situation?
A
Train a time-series model to predict the machines’ performance values. Configure an alert if a machine’s actual performance values significantly differ from the predicted performance values.
B
Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Use this heuristic to monitor server performance in real time.
C
Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Train a model to predict anomalies based on this labeled dataset.
D
Hire a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.