NCP-MCI-6.5 Exam Dumps : Nutanix Certified Professional - Multicloud Infrastructure (NCP-MCI) v6.5 exam

According to the Nutanix Volumes - Recommendations And Best Practices web search result3, two actions that the administrator should take when designing the Volume Group are:

• Distribute workload across multiple virtual disks: Use multiple disks rather than a single large disk for an application. Consider using a minimum of one disk per Nutanix node to distribute the workload across all nodes in a cluster. Multiple disks per Nutanix node may also improve an application’s performance. For performance-intensive environments, we recommend using between four and eight disks per CVM for a given workload.
• Enable RSS (Receive Side Scaling): Receive-side scaling (RSS) allows the system to use multiple CPUs for network activity. With RSS enabled, multiple CPU cores process network traffic, preventing a single CPU core from becoming a bottleneck. Enabling RSS within hosts can be beneficial for heavy iSCSI workloads. For VMs running in ESXi environments, RSS requires VMXNET3 VNICs. For Hyper-V environments, enable VMQ to take full advantage of Virtual RSS.

According to the web search results, the policy that should be used to meet this requirement is an Isolation Environment Policy. An Isolation Environment Policy is a type of security policy that can be created in Prism Central using Flow Network Security, which is a feature that provides microsegmentation and network security for Nutanix environments1. An Isolation Environment Policy allows the administrator to isolate a group of VMs from another group of VMs based on their categories, and block all traffic between them regardless of direction2. This policy can be useful for creating isolated environments for testing, development, or compliance purposes2.

Nutanix leverages a method for determining the bands called ‘Generalized Extreme Studentized Deviate Test’. A simple way to think about this is similar to a confidence interval where the values are between the lower and upper limits established by the algorithm.

Another web source3 shows an example of how anomaly detection works in Nutanix Prism Central. In the video, you can see that when the observed value of a metric deviates significantly from the predicted value based on historical data, an anomaly event is triggered and displayed on a chart.

Therefore, by comparing the observed values with the predicted values based on historical data, Nutanix anomaly detection can identify abnormal behavior and alert you accordingly.