Question-56: A period of time during which users are unable to access data stored in the cloud is referred to as downtime. It's very prevalent among companies that use cloud services like Amazon Web Services, Microsoft Azure, or Google Cloud. It often does not adhere to a defined schedule and has a high degree of unpredictability. After doing an analysis of TerramEarth's business need to cut downtime, you discovered that the company can accomplish the bulk of the time savings they need by cutting the amount of time customers have to wait for components. You have decided on concentrating your efforts on shortening the total reporting time of three weeks. Which improvements to the ways in which the firm does things do you advocate making?
A. Change the format from CSV to binary, switch from FTP to SFTP for transport, and use machine learning to figure out how to analyse metrics.
B. Switch from FTP to streaming transport, from CSV to binary format, and create an analysis of metrics using machine learning.
C. Get 80% of the fleet connected to cellular networks, switch from FTP to streaming transport, and use machine learning to analyse metrics.
D. Switch from FTP to SFTP for transport, use machine learning to analyse metrics, and add a fixed factor to dealer local inventory.
Correct Answer
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: 3 Explanation: Because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. A is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. Option-2 is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. Option-4 is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all. Option-1 is not correct because machine learning analysis is a good means toward the end of reducing downtime, but shuffling formats and transport doesn't directly help at all. Option-2 is not correct because machine learning analysis is a good means toward the end of reducing downtime, and moving to streaming can improve the freshness of the information in that analysis, but changing the format doesn't directly help at all. Option-3 is correct because using cellular connectivity will greatly improve the freshness of data used for analysis from where it is now, collected when the machines are in for maintenance. Streaming transport instead of periodic FTP will tighten the feedback loop even more. Machine learning is ideal for predictive maintenance workloads. Option-4 is not correct because machine learning analysis is a good means toward the end of reducing downtime, but the rest of these changes don't directly help at all.