Which of the following statements is true about the On-Cloud Machine Learning?

The assertion that On-Cloud Machine Learning avoids storing bad hashes on the host is accurate. This approach leverages the cloud's capabilities to utilize vast amounts of data and advanced algorithms for analysis and detection, which allows it to minimize the use of local resources and avoid the risks associated with malicious hash storage on the endpoint itself. By operating primarily in the cloud, it ensures that potentially harmful signatures or indicators of compromise (IOCs) aren't retained locally, thus enhancing security and reducing the attack surface.

The cloud-based nature of the machine learning model means it can continuously learn and adapt without the need for persistent local storage of potentially harmful data. This makes it more efficient and responsive to emerging threats, as it can rely on centralized processing power and updated intelligence without compromising the security of the endpoint.

In contrast, the other statements do not accurately reflect the functionality of On-Cloud Machine Learning. For instance, its reliance on internet connectivity is not a limitation but a fundamental characteristic of cloud solutions. Similarly, it does not inherently interfere with existing Anti-Virus tools; rather, it complements them by enhancing detection capabilities. Lastly, while it does analyze file attributes for classification and threat detection purposes, it does not function entirely independently of them, as file characteristics can play a role