NCP-CN Exam Dumps - Nutanix Certified Professional (NCP) Questions and Answers

The Nutanix Kubernetes Platform (NKP) integrates Velero, an open-source tool, for backup and restore operations as part of its Day 2 operations. The NKPA course explains that after configuring a Backup Storage Location (e.g., an AWS S3 bucket) and creating a backup, administrators can view details of the backup using the Velero CLI. The correct command to view the details of a backup named testbackup is velero backup describe testbackup. This command provides a detailed description of the backup, including its status, resources included, and storage location.

The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “To inspect a Velero backup in NKP, use the velero backup describe command to display detailed information about the backup, such as its creation time, expiration, and included resources.” The backup name (testbackup) is specified as created by the team, and no prefix like aws-velero- is indicated in the question, making option C the correct choice.

Incorrect Options:

A. kubectl get backupstoragelocations -n ${testbackup} -o yaml: This command retrieves Backup Storage Location objects, not backup details. The namespace ${testbackup} is also incorrect, as Velero resources are typically in a specific namespace (e.g., velero).

B. velero backup describe aws-velero-testbackup: The backup name is testbackup, not aws-velero-testbackup. The NKPA course does not indicate any prefix for the backup name.

D. kubectl get backupstoragelocations -n ${WORKSPACE_NAMESPACE} -o yaml: This retrieves Backup Storage Locations, not the backup itself, and does not provide backup details.

The NKPA course specifies the supported operating systems for NKP clusters deployed using the Nutanix provisioning method (CAPX), which leverages Cluster API for Nutanix (CAPX) to provision clusters on Nutanix AHV. The supported OS platforms for CAPX are Rocky Linux and Ubuntu, as these distributions are tested and optimized for Nutanix infrastructure and Kubernetes requirements.

Rocky Linux is a CentOS replacement adopted by Nutanix after CentOS 8’s end-of-life in 2021, providing a stable, enterprise-grade OS. Ubuntu, particularly LTS versions like 20.04 or 22.04, is widely supported due to its compatibility with Kubernetes and Nutanix AHV. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “When deploying NKP with the Nutanix provisioning method (CAPX), the supported OS platforms are Rocky Linux and Ubuntu, ensuring compatibility with Nutanix AHV and Kubernetes.” These OS images are typically prepared using NKP Image Builder (NIB) to include necessary components like kubeadm and containerd.

Incorrect Options:

A. CentOS and Rocky Linux: CentOS 8 is no longer supported post-2021, and Nutanix has shifted to Rocky Linux.

C. Flatcar, Rocky Linux, and Ubuntu: Flatcar Container Linux is not a supported OS for CAPX in NKP deployments.

D. CentOS and Ubuntu: CentOS is not supported, as noted above.

According to the NKPA 6.10 documentation, the FIPS Compliant Build feature is exclusively available for Enterprise tier licensing. This ensures compliance with NIST security standards and provides hardened cryptographic capabilities throughout the Kubernetes cluster stack.

Exact extract from documentation:

“The FIPS-compliant build feature is included in the NKP Enterprise tier, enabling deployments in environments that require strict security compliance.”

To create a FIPS-compliant NKP deployment in an air-gapped environment, the following must be done:

Verify the OS (RHEL) itself is in FIPS mode.

When building the image, include both the offline fips and fips overrides to ensure the image and components are built in compliance.

When deploying the cluster, include FIPS-specific references for both Kubernetes and etcd components.

Exact extract:

“For FIPS deployments, ensure the OS is in FIPS mode, and include both offline fips and fips overrides during image creation to ensure compliant images and deployment.”

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Kubernetes Platform Administration (NKPA) Course:

The NKPA course outlines the process of adding GPU-enabled worker nodes to an existing NKP-managed Kubernetes cluster, such as the production cluster in this scenario. The first step in this process is to ensure that a GPU-compatible OS image is available for the new worker nodes, as GPU support requires specific drivers and configurations (e.g., NVIDIA drivers) that are not included in standard OS images.

The correct first step is to create a GPU-compatible OS image using the command:

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nkp create image nutanix --gpu \

--gpu-name=${GPU_NAME} \

--cluster=${NUTANIX_CLUSTER_NAME} \

--endpoint=${NUTANIX_PC_ENDPOINT} \

--subnet=${NUTANIX_SUBNET} ubuntu-22.04

(Option D). This command uses the NKP CLI to create a machine image based on Ubuntu 22.04, tailored for Nutanix AHV infrastructure (nutanix) with GPU support enabled (--gpu). The --gpu-name flag specifies the GPU type (e.g., NVIDIA GPU model), and other parameters define the Nutanix cluster, Prism Central endpoint, and subnet for image creation. The resulting image includes the necessary NVIDIA drivers and dependencies, making it suitable for GPU-enabled worker nodes. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “Before adding GPU-enabled workers to an NKP cluster on Nutanix, the first step is to create a GPU-compatible OS image using nkp create image nutanix --gpu, ensuring the image includes the required GPU drivers for the target infrastructure.”

This image is then used in subsequent steps (like Option A) to create a node pool with GPU-enabled workers. Without this image, the node pool creation in Option A would fail due to the lack of a suitable --vm-image.

Incorrect Options:

A. Create a nodepool of workers with GPU: This step requires a pre-existing GPU-compatible OS image (specified via --vm-image). Since the question does not indicate that such an image already exists, creating the image (Option D) must happen first.

B. Add the GPU Operator to the new workers: The NVIDIA GPU Operator can be installed to manage GPU resources, but this step occurs after the GPU-enabled workers are added to the cluster, not as the first step.

C. Configure Multi-Instance GPU (MIG): MIG configuration is an advanced GPU setup step that occurs after the workers are added and the GPU Operator is installed, not as the first step.

The NKPA course addresses performance issues in NKP clusters, such as high CPU and memory consumption on worker nodes, by recommending scaling options to distribute workloads more effectively. The Kubernetes cluster in the scenario has 4 workers, each with 8 vCPUs and 32 GB RAM, and is running out of resources. The most effective solution is to add more workers to the cluster to increase overall capacity and balance the load, rather than modifying existing workers or reducing application replicas.

The correct action is to add one more worker using the command nkp scale nodepools ${NODEPOOL_NAME} --replicas=5 --cluster-name=${CLUSTER_NAME} -n ${CLUSTER_WORKSPACE} (Option D). This command scales the specified node pool to 5 replicas (from the current 4), adding one additional worker to the cluster. The new worker will have the same configuration (8 vCPUs, 32 GB RAM) as the existing workers, providing additional capacity to alleviate resource contention. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “To address performance issues due to resource exhaustion in an NKP cluster, scale out by adding workers using nkp scale nodepools --replicas --cluster-name -n .”

Incorrect Options:

A. Call tech support to investigate: While support can help, the issue is clearly resource exhaustion, which can be resolved by scaling the cluster—a task the Platform Engineer can perform directly.

B. Ask developers to lower application replicas: Reducing replicas may decrease resource usage but could impact application availability or performance, which is not ideal for a development cluster.

C. Add more CPU and memory to workers with nkp scale --cpu 16 --memory 64: The nkp scale command does not support --cpu and --memory flags for resizing existing workers. Resizing VMs typically requires updating the node pool configuration and replacing nodes, which is more complex than adding new workers.

The NKPA course specifies that the NKP CLI is the primary tool for deploying NKP clusters with advanced configurations, such as those required for a dark site environment, custom Service and Pod CIDR blocks, and Nutanix AHV clusters. The NKP CLI (nkp) provides granular control over cluster creation, allowing administrators to specify custom networking parameters and air-gapped deployment settings via command-line flags or configuration files.

For a dark site deployment, the NKP CLI uses an Air-Gapped Bundle to provide all necessary dependencies. Custom CIDR blocks are configured using flags like --service-cidr and --pod-cidr during the nkp create cluster command. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “The NKP CLI is used to deploy clusters with custom configurations, including dark site readiness and custom networking, on Nutanix AHV clusters.” For example: nkp create cluster --air-gapped --service-cidr 10.96.0.0/12 --pod-cidr 192.168.0.0/16 --infrastructure nutanix-ahv.

Incorrect Options:

A. kubectl CLI: kubectl manages Kubernetes resources, not NKP cluster deployment.

B. NKP GUI: The GUI supports basic deployments but lacks the flexibility for custom CIDR and dark site configurations.

D. OCP CLI: OpenShift (OCP) is a separate platform, not used for NKP deployments.

For deployments in air-gapped environments where there is no external Internet connectivity, the --airgapped parameter is required. This instructs the NKP deployment to rely solely on internal resources, using pre-staged images and local container registries, ensuring that no external network dependencies cause deployment failures.

The primary benefit of a private registry is to ensure security and privacy for container images, especially when dealing with sensitive data and compliance requirements (such as financial or government use cases). While secure connections to public registries like DockerHub or GitHub container registries are possible, using a private registry ensures full control over image access and auditing.

Key reference:

“Private registries ensure images remain within the security and compliance boundaries of the enterprise, avoiding potential risks associated with public SaaS registries.”

According to the NKPA 6.10 documentation, licensing in an NKP cluster is constrained by the total number of vCPUs assigned to all nodes (workers and control planes). In this scenario, the cluster has a maximum of 150 vCPUs available.

The existing output shows 10 worker nodes, each with 8 vCPUs (10×8=80 vCPUs) and 3 control plane nodes, each with 4 vCPUs (3×4=12 vCPUs).

Current vCPU usage: 80 + 12 = 92 vCPUs.

Remaining vCPUs: 150 - 92 = 58 vCPUs.

Option A:

Adding 6 control plane nodes (6×4=24 vCPUs).

This would increase control planes to 9 total (3 existing + 6 new). Control plane nodes would consume 9×4=36 vCPUs.

Worker nodes remain at 80 vCPUs.

Total vCPU usage: 36 + 80 = 116 vCPUs (still under 150 vCPUs).

However, it’s unusual and unnecessary to have 9 control planes in a typical NKP setup. NKPA and NCP-CN best practices indicate typically no more than 3 or 5 control planes for HA. Therefore, this is technically possible but not recommended or standard practice. So not selected.

Option B:

Expanding the existing worker nodepool by 8 nodes (8×8=64 vCPUs).

This would add 64 vCPUs to the current 92 vCPUs:

92 + 64 = 156 vCPUs → exceeds licensing limit.

Correction: The question specifies “expand up to 8 nodes,” so it’s possible to expand by only 7 nodes (7×8=56 vCPUs).

92 + 56 = 148 vCPUs → within licensing limits.

Thus, expanding by up to 7 nodes is valid.

Option C:

Creating a new worker nodepool with 10 nodes (10×8=80 vCPUs).

This would add 80 vCPUs to the existing 92 vCPUs:

92 + 80 = 172 vCPUs → exceeds 150 vCPU licensing limit.

Not valid.

Option D:

Expanding the original worker nodepool by up to 4 nodes (4×8=32 vCPUs) = +32 vCPUs.

Adding a new nodepool of 6 nodes with 4 vCPUs/node (6×4=24 vCPUs) = +24 vCPUs.

Total increase: 32 + 24 = 56 vCPUs.

Total vCPU usage: 92 + 56 = 148 vCPUs → within the 150 vCPU limit.

Thus, valid.

Extract Reference:

NKPA 6.10 – “Cluster Sizing and Licensing”

Nutanix Kubernetes Platform Administration (NKPA) – Licensing Considerations

"The total number of vCPUs for all nodes (control planes and workers) must not exceed the licensed vCPU capacity of the NKP deployment."

This confirms that Options B and D are the two valid approaches to expand the cluster while remaining compliant with licensing constraints.