NCS-CORE practice question 26 of 35
A systems administrator needs to add more VMs to their Nutanix cluster. Which two actions should the administrator perform to determine if the current cluster can accommodate the new VMs? (Choose two.)
Full set: 302 questionsTo determine if a cluster can host more VMs, an administrator must perform capacity planning. The primary Nutanix tool for this is Cluster Runway (B), which is available in Prism. It analyzes historical usage of CPU, memory, and storage to forecast how many days of capacity remain. This directly answers whether there is room for growth. A complementary action is to Utilize Optimize Resources (D). This feature, powered by X-Fit, identifies inefficiently configured VMs (e.g., oversized, inactive). By right-sizing these existing VMs, an administrator can reclaim wasted resources, thereby increasing the available capacity for new VMs.
Perform an Inventory with Life Cycle Management.: Life Cycle Management (LCM) inventories and updates software and firmware versions. It does not analyze or forecast resource capacity for workloads. Enable Deduplication and Erasure Coding.: These are storage efficiency features. While they can increase usable storage capacity, they do not provide a holistic capacity analysis for CPU and memory resources.
1. Nutanix. (2023). Prism Central Guide v2023.3. Chapter: "Planning". Section: "Capacity Runway". This section describes how the runway provides a view of the capacity (storage, CPU, and memory) of a registered cluster and estimates when the cluster might run out of resources.
2. Nutanix. (2023). Prism Central Guide v2023.3. Chapter: "Managing Virtual Machines (VMs) (Self-Service)". Section: "VM Right Sizing". This section explains how the "Optimize Resources" feature identifies oversized, constrained, inactive, and bully VMs to improve cluster efficiency.
3. Nutanix University. (2023). Nutanix Hybrid Cloud Fundamentals (NHCF). Module: "Prism Central". The courseware covers both Cluster Runway for capacity forecasting and VM efficiency features for resource optimization as key management tasks.
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