Topic 1 Question 94
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?
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.
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.
Develop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Test this heuristic in a production environment.
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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コメント(9)
- 正解だと思う選択肢: C
I would go for C, it is important to have a clear understanding of what constitutes a potential failure and how to detect it. A heuristic based on z-scores, for example, can be used to flag instances where the performance values of a machine significantly differ from its historical baseline.
👍 6mil_spyro2022/12/17 - 正解だと思う選択肢: B👍 3hiromi2022/12/19
What’s the difference between B and C?
👍 3studybrew2023/03/26
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