NCP-CN Exam Dumps : Nutanix Certified Professional - Cloud Native (NCP-CN-6.10)

When an attached cluster (e.g., an external cluster like EKS) is stuck in a 'deleting' state in the NKP UI, it indicates an issue with the reconciliation process in the NKP management cluster. The NKPA course explains that attached clusters are represented in NKP as KommanderCluster custom resources in the management cluster. To resolve a stuck deletion, the engineer must manually delete the KommanderCluster resource using kubectl in the context of the NKP management cluster.

The correct command iskubectl delete kommandercluster, executed in the context of the NKP management cluster (not the attached cluster). For example: kubectl delete kommandercluster -n . The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “If an attached cluster is stuck in a ‘deleting’ state, delete the corresponding KommanderCluster resource in the NKP management cluster using kubectl delete kommandercluster to remove it from management.” This ensures the cluster is fully detached and removed from the UI.

Incorrect Options:

A. kubectl delete cluster: There is no cluster resource type in this context; the correct resource is kommandercluster.

B. nkp delete kommandercluster: The nkp CLI does not have a delete kommandercluster subcommand.

D. nkp delete cluster in the attached cluster: This command is for deleting NKP-managed clusters, not detaching external clusters, and it should be run from the management cluster context.

The NKPA course highlights thatNKP Insightsis the platform’s integrated solution for observability and analytics, providing predictive analytics to detect current and future anomalies in Kubernetes clusters. NKP Insights leverages machine learning to analyze metrics, logs, and events, identifying performance issues and potential failures proactively. After deploying a new cluster, the administrator can enable NKP Insights as a platform application in the workspace to meet the predictive analytics requirement.

The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “NKP Insights provides predictive analytics and anomaly detection for Kubernetes clusters, enabling administrators to monitor and optimize cluster performance.” This is deployed via the NKP UI or CLI by enabling the Insights application in the workspace.

Incorrect Options:

B. NCM Intelligent Ops: Nutanix Cloud Manager (NCM) Intelligent Ops is for broader infrastructure management, not Kubernetes-specific analytics.

C. Nutanix Pulse: Pulse is a telemetry service for Nutanix support, not for analytics.

D. NKP AI Navigator: This is not a recognized NKP component in the NKPA documentation.

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 tocreate a GPU-compatible OS imageusing 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.