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| Vendor: | Nutanix |
|---|---|
| Exam Code: | NCP-CN |
| Exam Name: | Nutanix Certified Professional - Cloud Native v7.5 |
| Exam Questions: | 113 |
| Last Updated: | October 6, 2026 |
| Related Certifications: | Nutanix Certified Professional |
| Exam Tags: | Professional Level DevOps engineersCloud Engineers |
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A Kubernetes administrator needs to deploy a new Kubernetes cluster into a new workspace. This cluster requires a predictive analytics solution that detects current and future anomalies. Which option does the administrator need to deploy after the cluster is ready?
When deploying a Kubernetes cluster that requires predictive analytics for detecting anomalies, the administrator should deploy Nutanix's monitoring and observability service (or equivalent anomaly detection solution) after cluster provisioning. This solution provides machine learning-based capabilities to detect current and future anomalies in the cluster, workloads, and infrastructure. It integrates with NKP's observability stack and provides predictive insights for proactive cluster management.
Which two statements are true regarding deploying NKP on an Azure Kubernetes Service (AKS) Infrastructure? (Choose two.)
Two true statements regarding deploying NKP on Azure Kubernetes Service (AKS) Infrastructure are:
Additional true statements may include:
False statements to avoid:
A Platform Engineer is attaching existing Kubernetes clusters to NKP, but some of them have network restrictions, so there is a need to use Secure Tunnel. The Platform Engineer needs to ask the Security Engineer to modify the firewall rules.
What must the firewall rules allow on the attached cluster network?
When using Secure Tunnel to attach clusters with network restrictions, firewall rules must allow outbound HTTPS (port 443) traffic from the attached cluster network to the NKP management cluster. Specifically, the firewall rules should allow: (1) Outbound TCP port 443 (HTTPS) for secure communication between the Secure Tunnel agent on the attached cluster and the NKP management cluster; (2) DNS resolution to enable the attached cluster to reach the NKP management cluster by hostname; (3) The specific IP address or domain name of the NKP management cluster's Secure Tunnel endpoint. The Secure Tunnel creates an encrypted, outbound-only connection from the attached cluster to the management cluster, eliminating the need for inbound firewall access. This approach allows clusters in restricted networks to be managed by NKP without exposing them to inbound network access.
separate Kubernetes clusters for production and development because the software deployed in production is different than development. Now, all production clusters for all business units are standardized and all clusters for development are standardized as well.
The company recently acquired NKP Ultimate licenses for all Kubernetes clusters.
How can the company control the standard configuration for both environments in all clusters (production and development)?
To control the standard configuration for both production and development environments across all clusters, the company should use NKP's Fleet Management capabilities combined with Kustomization or FluxCD. However, the most appropriate approach in NKP v2.x is to use Workspaces and Project or Workspace-level policies to enforce standardized configurations. Alternatively, they can leverage GitOps through FluxCD to maintain declarative configurations across all clusters, or use Management Clusters with standardized cluster profiles/templates that define baseline configurations for both production and development clusters. The NKP Ultimate license enables advanced fleet management features that allow centralized policy enforcement and configuration management.
A development Kubernetes cluster deployed with NKP is having performance issues. The Cloud Engineer commented that worker VMs are consuming a lot of CPU and RAM. The Platform Engineer took a look at the CPU and RAM statistics with Grafana and confirmed that the worker VMs are running out of CPU and memory. The Kubernetes cluster has 4 workers with 8 vCPUs and 32 GB RAM. What could the Platform Engineer do?
To resolve the performance issue where worker VMs are running out of CPU and memory, the Platform Engineer could: (1) Scale up the worker nodes by increasing vCPU and RAM resources allocated to each VM, (2) Scale out by adding additional worker nodes to distribute the workload across more nodes, (3) Optimize pod resource requests and limits to ensure workloads are using resources efficiently, (4) Remove unnecessary or idle workloads consuming resources, or (5) Implement resource quotas and pod disruption budgets to manage resource allocation. Given that all 4 workers are experiencing resource exhaustion, scaling out (adding more nodes) would be the most immediate solution, though scaling up individual nodes may also be necessary. Additionally, the engineer should investigate what workloads are consuming resources and whether they are properly configured with appropriate requests and limits.
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