NCP-AI practice question 3 of 15

An AI/ML administrator is monitoring a Nutanix Enterprise AI cluster and receives an alert that the cluster's health status is Critical. The…

An AI/ML administrator is monitoring a Nutanix Enterprise AI cluster and receives an alert that the cluster's health status is Critical. The administrator logs into the NAI Dashboard and gathers the following information: The Infrastructure Summary component is marked as Critical (red status). A system message indicates that their newly added Custom Chatbot service is waiting for available resources to start. A resource usage summary shows that CPU usage is at or near 100%. Other services are running but are responding more slowly than usual. The cluster is currently not configured to automatically add more resources when needeD. Based on the information that the administrator gathered, what is the most appropriate action that the administrator should take to remediate the issue?

Answer options

A: Enable automatic resource scaling within NKP and allow for more CPU resources to be allocated to the clusters. Correct answer

B: Enable automatic resource scaling within NAI to allow for more CPU resources to be allocated to the clusters.

C: Restart the Kubernetes node in a rollout fashion within Prism Central.

D: Restart the Kubernetes node in a rollout fashion within NAI DashboarD.

Correct Answer:

A. Enable automatic resource scaling within NKP and allow for more CPU resources to be allocated to the clusters.

Explanation

The Custom Chatbot pod is stuck in Pending because no CPU is free on the Kubernetes worker nodes (usage ≈ 100 %). The only durable fix is to let the platform add additional vCPUs/nodes. In a Nutanix Enterprise AI deployment that runs on Nutanix Kubernetes Platform (NKP/Karbon), cluster-level autoscaling is enabled from the NKP console; NAI itself can only change service-level limits, not create capacity. Enabling NKP cluster-autoscaler therefore supplies the extra CPU the Infrastructure Summary reports as missing and clears the Critical state.

Why Incorrect

B. NAI scaling modifies quota for AI services only; it cannot add worker nodes, so CPU starvation remains.
C. Rolling-restarting nodes merely recycles the same capacity; high CPU contention returns immediately.
D. Same as C, but via NAI UI—does not increase capacity and prolongs outage.

References

1. Nutanix Karbon Platform Services 2.4 Administration Guide, “Using Cluster Autoscaler,” pp. 91-93.

2. Nutanix Enterprise AI Operations Guide v1.1, §4.3 “Infrastructure Summary – resource exhaustion,” p. 37.

3. Nutanix KB-10367 “Custom Service Pending – Insufficient Resources,” para. 2-3, 2023-07-12.

Keep practicing

Take the free NCP-AI practice test

Ten exam-style questions with answers and explanations, plus the exam facts and study guides.

Start the free practice test