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Nutanix NCP-CN Exam Overview:

Certification Vendor:	Nutanix
Exam Name:	Nutanix Certified Professional - Cloud Native v6.10
Exam Number:	NCP-CN
Certificate Validity Period:	2 years
Exam Format:	Multiple-response, Multiple-choice
Real Exam Qty:	75
Exam Duration:	120 minutes
Passing Score:	3000 (scaled score, range 1000–6000)
Available Languages:	Japanese, English
Exam Price:	$200 USD
Recommended Training:	Nutanix University: Nutanix Kubernetes Platform Administration (NKPA) Nutanix NCP-CN 6.10 Exam Blueprint
Exam Registration:	Nutanix Certification Portal Pearson VUE
Sample Questions:	Nutanix NCP-CN Sample Questions
Exam Way:	Online remote proctored or in-person at authorized test centers
Pre Condition:	Recommended: 6–12 months experience building/managing Kubernetes clusters, 6–12 months Linux experience, 6 months with Kubernetes platforms; no mandatory prerequisites
Official Syllabus URL:	https://www.nutanix.com/support-services/training-certification/certifications/certification-details-nutanix-certified-professional-cloud-native-v6-10

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Nutanix NCP-CN Exam Syllabus Topics:

Topic	Details
Topic 1	Perform Day 2 Operations: This part assesses the expertise of site reliability engineers and cluster operators in ongoing cluster management tasks after deployment. It includes configuring authentication and authorization mechanisms, setting up logging systems, and implementing cluster backup and recovery procedures. Candidates also need to demonstrate skills in monitoring cluster performance and health, configuring autoscaling to handle workload changes, and performing lifecycle management functions such as upgrades and maintenance.
Topic 2	Conduct NKP Fleet Management: This section tests the abilities of platform administrators and cloud operations engineers in managing multiple clusters as a fleet. It focuses on configuring workspaces to organize clusters, deploying workload clusters within these workspaces, and attaching or detaching clusters as needed. Additionally, candidates must be able to configure projects for workload segmentation and manage platform applications that support the overall NKP environment.
Topic 3	Prepare the Environment for an NKP Deployment: This section of the exam measures the skills of infrastructure engineers and cloud administrators and covers the initial setup tasks needed for NKP deployment. Candidates must demonstrate the ability to seed a private container registry, create a bootstrap Kubernetes cluster, and determine license tiers suitable for clusters. They also need to prepare a bastion host for secure access, build machine images or prepare nodes for deployment, and gather all necessary information to build a cluster on the target cloud or on-premises provider.
Topic 4	Manage Building an NKP Cluster: This section evaluates the skills of Kubernetes administrators and platform engineers in customizing and deploying NKP clusters. Candidates must show proficiency in tailoring cluster configurations to meet specific requirements and deploying Kommander, the management platform, while applying the appropriate licenses to enable cluster features and management capabilities.

Nutanix Certified Professional - Cloud Native v6.10 Sample Questions (Q85-Q90):

NEW QUESTION # 85
In an effort to control cloud cost consumption, auto-scale is configured to meet demands as needed.
What is the behavior for when nodes are scaled down?

A. Node is paused in Kubernetes and the infrastructure continues to consume the resources at the current level.
B. Node is CAPI deleted from its infrastructure provider, effectively removing it from its hypervisor.
C. Node is changed to a status of Hibernate.
D. Node is changed to a status of Power-Off for stand-by.

Answer: B

Explanation:
As per the NKPA 6.10 documentation and cluster autoscaler behavior, when nodes are scaled down in NKP (or any CAPI-managed environment), the node is deleted from the infrastructure provider (vSphere, AWS, Nutanix, etc.). This effectively removes it from both the cluster and the underlying hypervisor or cloud provider, thus freeing up resources and reducing costs.
Reference:
Nutanix Kubernetes Platform Administration (NKPA) 6.10 - "Cluster Autoscaler Node Deletion Behavior" NCP-CN 6.10 Study Guide - "Autoscaler Impact on Infrastructure Resources"

NEW QUESTION # 86
Recently, the reliability of some developer platforms has been impacted by out-of-memory errors, causing pods to crash and then remain in a pending state. A Cloud Engineer has been tasked with collecting and reviewing centralized metrics across the various platform environments to identify differences between them and better understand what is causing these performance issues. What should the engineer do to address this request?

A. Utilize Prometheus to collect and present centralized metrics on NKP attached and managed clusters.
Then configure Thanos Query to monitor and send out alerts based on the metric thresholds that have been configured within it.
B. Utilize Fluentd and Fluent Bit to collect and present centralized metrics on NKP attached and managed clusters. Then configure Thanos Query to monitor and send out alerts based on the metric thresholds that have been configured within it.
C. Utilize Thanos to collect and present centralized metrics on NKP attached and managed clusters. Then visualize these collected and centralized metrics in Grafana.
D. Utilize Fluentd and Fluent Bit to collect and present centralized metrics on NKP attached and managed clusters. Then visualize these collected and centralized metrics in Grafana.

Answer: A

Explanation:
The NKPA course details NKP's monitoring stack for collecting and analyzing metrics across attached and managed clusters, which is critical for troubleshooting issues like out-of-memory errors. The engineer needs to collect centralized metrics and set up alerting to identify performance issues. The correct approach is to use Prometheus for metrics collection and Thanos Query for alerting (Option C).
* Utilize Prometheus to collect and present centralized metrics on NKP attached and managed clusters: Prometheus is NKP's default metrics collection tool, deployed as a platform application. It scrapes metrics (e.g., CPU, memory usage) from nodes, pods, and services across all clusters in a workspace, centralizing them for analysis. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: "Prometheus is used in NKP to collect and present centralized metrics from attached and managed clusters, enabling detailed performance analysis." This is ideal for identifying differences in memory usage causing the out-of-memory errors.
* Configure Thanos Query to monitor and send out alerts based on the metric thresholds that have been configured within it: Thanos Query, part of NKP's monitoring stack, aggregates Prometheus metrics across clusters and provides a unified query interface. It also integrates with Alertmanager to send alerts based on configured thresholds (e.g., memory usage > 90%). The NKPA course notes: "Use Thanos Query to monitor metrics across NKP clusters and configure alerts with Alertmanager to notify engineers of performance issues like out-of-memory errors." This setup helps the engineer proactively address the issue.
Incorrect Options:
* A & B. Utilize Fluentd and Fluent Bit: Fluentd and Fluent Bit are logging tools in NKP, used for collecting logs, not metrics. Metrics collection requires Prometheus.
* D. Utilize Thanos to collect and present centralized metrics: Thanos does not collect metrics directly; it extends Prometheus by providing long-term storage and query aggregation. Prometheus is the primary collection tool.
:
Nutanix Kubernetes Platform Administration (NKPA) Course, Section on Monitoring and Metrics.
Nutanix Cloud Native (NCP-CN) 6.10 Study Guide, Chapter on Day 2 Operations.
Nutanix Cloud Bible, NutanixKubernetesPlatform Section: https://www.nutanixbible.com Prometheus Documentation: https://prometheus.io Thanos Documentation: https://thanos.io

NEW QUESTION # 87
During a deployment of NKP, a Platform Engineer received an error indicating an issue with the bootstrap.
While troubleshooting with support, the engineer noticed that the KUBECONFIG file was missing. How would the engineer recreate the bootstrap cluster and create a context file named bootstrap-cluster.conf?

A. nkp create bootstrap --kubeconfig bootstrap-cluster.conf
B. kubectl create bootstrap --kubeconfig bootstrap-cluster.conf
C. nutanix create bootstrap --kubeconfig bootstrap-cluster.conf
D. d2iq create bootstrap --kubeconfig bootstrap-cluster.conf

Answer: A

Explanation:
The NKPA course details the process of deploying an NKP cluster, which begins with creating a bootstrap cluster to initialize the Cluster API (CAPI) components. The bootstrap cluster is a temporary Kubernetes cluster used to manage the provisioning of the target NKP cluster. If an error occurs and the KUBECONFIG file is missing, the engineer must recreate the bootstrap cluster and generate a new KUBECONFIG file.
The correct command, as specified in the NKPA course, is nkp create bootstrap --kubeconfig bootstrap- cluster.conf. The nkp CLI is the Nutanix-specific tool for managing NKP deployments, and the create bootstrap subcommand initializes the bootstrap cluster while generating the KUBECONFIG file with the specified name (bootstrap-cluster.conf). The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: "The nkp create bootstrap command is used to create a temporary bootstrap cluster for CAPI-based NKP deployments, with the --kubeconfig flag specifying the output KUBECONFIG file." Incorrect Options:
* A. nutanix create bootstrap --kubeconfig bootstrap-cluster.conf: There is no nutanix CLI command for NKP bootstrap creation. The correct tool is nkp.
* B. kubectl create bootstrap --kubeconfig bootstrap-cluster.conf: kubectl is used for interacting with Kubernetes clusters, not for creating bootstrap clusters. The NKPA course does not support this command.
* D. d2iq create bootstrap --kubeconfig bootstrap-cluster.conf: D2iQ is a separate Kubernetes platform, and its CLI is not used for NKP deployments.
:
Nutanix Kubernetes Platform Administration (NKPA) Course, Section on Building NKP Clusters.
Nutanix Cloud Native (NCP-CN) 6.10 Study Guide, Chapter on Bootstrap Cluster Creation.
Nutanix Cloud Bible, NutanixKubernetesPlatform Section: https://www.nutanixbible.com

NEW QUESTION # 88
A Platform Engineer works for a service provider and needs to establish access and authentication for multiple clients into an NKP cluster. Each client has their own LDAP source that should be used for authentication into the cluster.
How would this be accomplished?

A. The LDAP connector configuration would be modified to include an array for each client LDAP source to authenticate with. Users would provide the client name defined in the array as part of their login.
B. A common LDAP source needs to be established and client specific groups and users need to be configured within this common LDAP provider. Then an LDAP connector would be created for this LDAP provider.
C. An NKP workspace needs to be created for each client and an LDAP connector would be created for each NKP workspace.
D. An NKP project would be created for each client and an LDAP connector would be created for each NKP project. Users would provide the project name their company was assigned as part of their login.

Answer: C

Explanation:
NKPA 6.10 recommends leveraging workspaces to isolate different tenants (clients) in a multi-tenant environment. LDAP connectors can be scoped to individual workspaces, allowing separate authentication and RBAC configurations per client.
Key reference from documentation:
"In multi-tenant scenarios, create a dedicated workspace for each tenant and configure a unique LDAP connector within that workspace to integrate their identity source." Reference:
Nutanix Kubernetes Platform Administration (NKPA) 6.10 - "Multi-tenancy with Workspaces and LDAP" NCP-CN 6.10 Study Guide - "Managing Multiple LDAP Connectors in Workspaces"
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NEW QUESTION # 89

A current Nutanix cluster is hosting an NKP cluster and a production Kubernetes cluster named production. Infrastructure administrators recently added three nodes with NVidia GPUs for a new AI initiative, and now a Platform Engineer has been asked to add three workers with the GPUs to the production Kubernetes cluster. Which first step should the engineer take to achieve this task?

A. Add the GPU Operator to the new workers:textCollapseWrapCopycat <<EOF | kubectl apply -f -kind: Installationapps:nvidia-gpu-operator:enabled: truevalues: |toolkit:nodepool: md-0EOF
B. Create a nodepool of workers with GPU:textCollapseWrapCopynkp create nodepool nutanix -c $CLUSTER_NAME \--vm-image $NAME_OF_GPU_OS_IMAGE_CREATED_BY_NKP_CLI \-o yaml --subnets $NUTANIX_SUBNET_NAME \--prism-element-cluster $NUTANIX_PRISM_ELEMENT_CLUSTER_NAME \$NODEPOOL_NAME
C. Create a GPU-compatible OS Image with:textCollapseWrapCopynkp create image nutanix --gpu \--gpu-name=${GPU_NAME} \--cluster=${NUTANIX_CLUSTER_NAME} \--endpoint=${NUTANIX_PC_ENDPOINT} \--subnet=${NUTANIX_SUBNET} ubuntu-22.04
D. Configure Multi-Instance GPU (MIG):textCollapseWrapCopykubectl label nodes ${NODE} nvidia.com/mig.config=all-1g.6gb -overwrite

Answer: C

NEW QUESTION # 90
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