Exam Professional Machine Learning Engineer topic 1 question 94 discussion - ExamTopics


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Problem Statement

An international company managing a large fleet of on-premises servers needs a predictive maintenance solution to detect potential server failures using monitoring data (CPU/memory consumption). The incident data is unlabeled.

Proposed Solutions

  • A. Train a time-series model to predict performance values and alert on significant deviations.
  • B. Use a heuristic (e.g., z-score) to label historical data and train an anomaly prediction model.
  • C. Develop a heuristic (e.g., z-score) to label historical data and test it in production.
  • D. Hire analysts to manually label data and then train a model.

Suggested Answer and Rationale

The suggested answer is C. Before investing in complex model training or manual data labeling, testing a simple heuristic in a production environment allows for quick validation and iterative improvement, ensuring the chosen approach is practical and effective.

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You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. What should you do first?

  • 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. Implement 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.
  • C. Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Test this heuristic in a production environment.
  • 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.
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Suggested Answer: C 🗳️

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