NCP-CN Nutanix Certified Professional - Cloud Native (NCP-CN-6.10) Questions and Answers

NKP uses the Cluster Autoscaler to scale worker node replicas up or down. This ensures that resource availability dynamically matches workload demand by adjusting the number of worker nodes. Pod-level autoscaling (Horizontal-Pod autoscaler) is also supported for pod scaling but is not responsible for node pool expansion or compaction.

Key Reference:

“NKP uses the Cluster Autoscaler to dynamically adjust worker node pool replicas in response to cluster resource demands.”

According to the NKPA 6.10 documentation, the critical system components for NKP nodes are:

kubelet

containerdThese components are deployed in the standard Linux path: /var/lib.

Exact extract from the documentation:

“The kubelet and containerd services require disk space in /var/lib on the host operating system. It’s essential to allocate sufficient storage in /var/lib to accommodate these core Kubernetes components.”

According to the NKPA 6.10 documentation and Nutanix licensing details, NKP Starter is bundled with the NCI Starter entitlement. This basic licensing tier includes essential capabilities for deploying and managing Kubernetes clusters within a Nutanix environment.

Key reference:

“NKP Starter is included with NCI Starter, providing foundational Kubernetes cluster lifecycle management features for small-scale or starter deployments.”

The NKPA 6.10 documentation outlines that in air-gapped environments where default pod network CIDR conflicts exist, the cluster manifest (created via the nkp create cluster command) should be updated to specify an alternate, non-conflicting pod CIDR block range before deployment.

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.”

The error message:

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failed to list clusters: command "docker ps -a --filter label=io.x-k8s.kind.cluster --format '{{.Label "io.x-k8s.kind.cluster"}}'" failed with error: exit status 1

indicates that the command failed while attempting to list docker containers with the given filter. The kind tool (used for creating a bootstrap cluster) depends on Docker as the container runtime.

Root cause:

If Docker is not running or a compatible container runtime is not available (such as containerd), the kind tool cannot list or interact with the required containers, resulting in this error.

Key Reference:

“When creating bootstrap clusters, the kind tool requires Docker to be installed and running. If Docker is not running, errors will be encountered listing or interacting with containers.”

Comprehensive and Detailed Explanation:

The correct procedure for deleting an NKP cluster involves using the nkp delete cluster command with the appropriate cluster name and namespace. This ensures that not only the Kubernetes resources but also the corresponding NKP resources (e.g., nodepools, Kommander integration) are deleted cleanly and consistently. Simply deleting the VMs does not clean up the associated NKP management objects. This approach is detailed in the NKP documentation for cluster lifecycle management, emphasizing the need to use the provided CLI commands for full removal.

The Secure Tunnel feature in NKP relies on HTTPS (TCP port 443) to establish secure, encrypted connections between the attached cluster and the management cluster, enabling fleet management even in restricted network environments.

Exact extract:

“Secure Tunnel uses TCP/443 (HTTPS) for establishing a secure connection between the attached cluster and NKP.”

The NKPA course explains the role of a bootstrap cluster in NKP deployments, particularly in hybrid architectures involving Nutanix on-prem and Amazon EKS. A bootstrap cluster is a temporary Kubernetes cluster created during the deployment process to initialize Cluster API (CAPI) components, which manage the lifecycle of the target NKP clusters. The primary function of the bootstrap cluster is to use Cluster API for infrastructure-agnostic provisioning (Option D).

CAPI enables NKP to provision clusters across different infrastructures (e.g., Nutanix AHV, AWS EKS) in a consistent, infrastructure-agnostic manner. The bootstrap cluster runs the CAPI controllers (e.g., CAPX for Nutanix, CAPA for AWS) that orchestrate the creation of the target clusters, abstracting the underlying infrastructure details. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “The bootstrap cluster in NKP uses Cluster API to provide infrastructure-agnostic provisioning, enabling consistent cluster creation across diverse environments like Nutanix AHV and Amazon EKS.” This ensures that the same CAPI-based workflow can deploy clusters on both on-prem Nutanix and cloud-based EKS.

Incorrect Options:

A. Increases security from a single point of entry: A bastion host, not a bootstrap cluster, provides a single point of entry for security. The bootstrap cluster is temporary and focuses on provisioning.

B. Simplifies the networking requirements for Kubernetes: Networking is managed by the target cluster’s CNI (e.g., Calico), not the bootstrap cluster.

C. Saves cost by minimizing resources required for provisioning: The bootstrap cluster is temporary and does not significantly impact costs; its purpose is provisioning, not cost-saving.

The NKPA course explains that NKP provides a role-based access control (RBAC) system to manage permissions within its platform, in addition to Kubernetes-native RBAC. For a development team needing limited permissions to perform specific tasks (deploying applications, scaling deployments, viewing logs and metrics) within a specific namespace, the appropriate access type in the NKP GUI is an NKP Role.

NKP Roles are predefined or custom roles within the NKP platform that map to Kubernetes RBAC permissions but are managed through the NKP UI for ease of use. They allow granular control over actions within a workspace or namespace, ensuring the team can perform their tasks (e.g., deploy, scale, get logs) without having access to other environments or cluster-wide resources. For example, an NKP Role like “Developer” or a custom role can be configured to grant edit permissions in the team’s namespace while restricting access elsewhere. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “In the NKP GUI, configure an NKP Role to grant limited permissions to a development team, allowing actions like deploying applications and viewing logs within their namespace while preventing unauthorized changes in other environments.”

Incorrect Options:

B. Cluster Role: A Kubernetes Cluster Role grants permissions across all namespaces, which is too broad for the team’s limited access requirement.

C. Cluster Admin: This grants full administrative access to the entire cluster, far exceeding the team’s needs and violating the principle of least privilege.

D. Kommander Role: Kommander is a management component in NKP, but “Kommander Role” is not a specific access type in the NKP GUI for this purpose.

The Nutanix Kubernetes Platform (NKP) uses the Kubernetes Image Builder (KIB) or Nutanix Image Builder (NIB) to create custom machine images for NKP management and worker nodes. These images are tailored to include specific versions of Kubernetes, container runtimes (e.g., containerd), and other dependencies. The NKPA course specifies that KIB relies on Ansible playbooks to configure the image, and the versions of packages like Kubernetes and containerd are defined in Ansible configuration files.

The correct location to verify the package versions is ansible/group_vars/all/defaults.yaml. This file contains the default variables used by Ansible during the image-building process, including the versions of Kubernetes and containerd. For example, it may include entries like kubernetes_version: 1.30.5 and containerd_version: 1.7.22. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “To ensure the correct versions of Kubernetes and containerd are used in KIB, check the ansible/group_vars/all/defaults.yaml file, which specifies the package versions for the image build.” The engineer can review and modify this file to confirm that the security team’s authorized versions (Kubernetes 1.30.5 and containerd 1.7.22) are being used.

Incorrect Options:

A. config/pulumi/vars/pulumi.kib.config: Pulumi is an infrastructure-as-code tool, but it is not used by KIB for specifying package versions. The NKPA course does not reference this file.

B. terraform/vars/default/terraform.tfvars: Terraform is used for infrastructure provisioning, not for defining software versions in KIB images.

D. The custom image's .env file: KIB does not use a .env file for version configuration; Ansible variables are used instead.

The Kubernetes Image Builder (KIB) is used to create CAPI-compliant machine images for NKP cluster nodes, often leveraging tools like Packer to build AMIs for AWS. When the KIB process fails, troubleshooting requires detailed logs to identify the root cause. The NKPA course recommends increasing the verbosity of the KIB command to capture detailed output, including AWS CLI commands and their responses.

The correct troubleshooting step is to rerun the KIB command with the verbosity level set to debug. This is typically done using the --verbose or -v flag (e.g., -v debug) to enable debug-level logging, which includes all AWS CLI commands, their outputs, and errors, written to a log file for review. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “To troubleshoot KIB failures, rerun the command with debug verbosity (-v debug) to log all operations, including AWS CLI interactions, to an exported log file for detailed analysis.” This approach helps pinpoint issues like permission errors, misconfigurations, or failures in image bundle uploads.

Incorrect Options:

A. Rerun with pause parameter: KIB does not support a pause parameter for step-by-step review, as per the NKPA course.

C. Instruct Packer not to delete the cloned AMI: While preserving the AMI can help, the course prioritizes debug logging for initial troubleshooting. Packer’s --on-error=abort flag exists, but it’s less direct than debug logs.

D. Instruct Ansible not to delete the cloned AMI: KIB primarily uses Packer, not Ansible, for AMI creation, making this option incorrect.

As per the “Nutanix Kubernetes Platform Administration (NKPA)” documentation, during the preparation of the environment for an air-gapped deployment, the first task involves installing critical software packages on the bastion host. The bastion host acts as a jump host in a secure network and requires tools such as yum-utils, bzip2, and wget to facilitate the retrieval and processing of required images and configuration files.

Specifically, in the NKPA 6.10 course under "Preparing the Air-gapped Environment", the documentation clearly states:

"The initial step for configuring the bastion host includes installing required pre-requisites like yum-utils, bzip2, and wget. This ensures that the host has the basic tools to download and manage required container images and other dependencies."

This step is explicitly called out as the first actionable item before proceeding to more advanced tasks like configuring public IP access or creating a bootstrap cluster. The need for these pre-requisite packages is foundational, as they are required to handle image downloading and manipulation in the subsequent steps of the air-gapped installation process.

The NKPA course clarifies that NKP licenses are based on the number of CPU cores in the infrastructure hosting the Kubernetes clusters. This licensing model applies to both on-premises (e.g., Nutanix AHV) and cloud-based deployments. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “NKP licenses are obtained based on the total number of CPU cores allocated to the clusters managed by the platform.” This core-based licensing ensures flexibility across different infrastructure types while aligning with the resource consumption of Kubernetes workloads.

Incorrect Options:

A. Flash: Flash storage is not a licensing unit for NKP.

B. CPU Sockets: Nutanix licenses for other products may use sockets, but NKP uses cores.

C. TiBs: Terabytes are used for storage licensing, not NKP.

The NKPA course outlines the specifics of integrating Azure Kubernetes Service (AKS) with NKP, focusing on fleet management and the deployment architecture. The correct statements are:

Existing AKS clusters must be attached to the Management cluster (Option A):NKP does not provision AKS clusters directly because AKS is a managed Kubernetes service. Instead, NKP attaches existing AKS clusters to its management plane for centralized management. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “For AKS integration, existing AKS clusters must be attached to the NKP Management cluster using the Attach Cluster functionality, as NKP does not provision AKS clusters.” This involves creating a service account with cluster-admin permissions in the AKS cluster and providing its kubeconfig to NKP.

NKP Management cluster with the Kommander component must be installed before installing NKP on AKS cluster (Option C):The Kommander component is part of the NKP Management cluster and provides the centralized management plane for fleet management, including AKS clusters. The NKPA course specifies that an NKP Management cluster with Kommander must be deployed before attaching or managing external clusters like AKS. The NCP-CN 6.10 Study Guide notes: “An NKP Management cluster with Kommander must be installed prior to integrating AKS clusters, as Kommander enables the fleet management capabilities for external clusters.”

Incorrect Options:

B. AKS best practices encourage building customized images: AKS best practices recommend using the default node images provided by Azure, which are optimized for AKS features like autoscaling and security. Custom images can lead to loss of functionality, as noted in previous questions (e.g., Q91).

D. AKS clusters can be deployed as Management or Pro NKP clusters: AKS clusters are attached, not deployed, by NKP. Additionally, AKS clusters cannot serve as NKP Management clusters; the Management cluster must be deployed on supported infrastructure like Nutanix AHV, AWS, or vSphere. “Pro NKP clusters” is not a valid term in NKP documentation—Pro refers to a license tier, not a cluster type.

The scenario describes an isolated environment in Antarctica, implying an air-gapped NKP deployment where the bastion host serves as the primary management point for deploying and managing NKP clusters without Internet access. The NKPA course outlines the essential tools that must be installed on a bastion host to facilitate NKP deployment and management in such environments.

The required programs are kubectl and nkp:

kubectl: This is the Kubernetes command-line tool used to interact with Kubernetes clusters, essential for managing cluster resources, debugging, and applying configurations during and after deployment.

nkp: The NKP CLI (nkp) is the primary tool for deploying, managing, and scaling NKP clusters, including creating bootstrap clusters, deploying clusters, and managing platform applications. In an air-gapped setup, the nkp CLI is critical for executing deployment commands using the Air-Gapped Bundle.

The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “For an air-gapped NKP deployment, ensure the bastion host has kubectl and the nkp CLI installed to manage the deployment process and interact with the resulting Kubernetes clusters.” These tools are sufficient for the engineer to deploy and operate NKP in an isolated environment.

Incorrect Options:

A. awscli and nkp: The awscli is specific to AWS environments, but the question does not specify AWS infrastructure. Additionally, in an isolated environment, awscli is less relevant without Internet access.

B. oc and kubectl: oc is the OpenShift CLI, not used for NKP deployments. NKP uses nkp CLI instead.

C. oc and az: az is the Azure CLI, and oc is for OpenShift—neither is relevant to NKP deployment.

The NKPA course explains that upgrading an NKP license to the Ultimate tier unlocks advanced features such as fleet management, air-gapped deployments, and additional platform services (e.g., monitoring, logging, backup/restore). These services are deployed on the Management cluster and require sufficient resources to operate effectively. A key prerequisite is to size the Control Plane nodes appropriately to support the installation of default platform services (Option C).

The Control Plane nodes in the NKP Management cluster run critical components like the API server, controller manager, and Kommander, as well as platform services such as Prometheus, Grafana Loki, and Velero. The Ultimate license increases resource demands due to the expanded functionality, and undersized Control Plane nodes can lead to performance issues or failed deployments. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “Before upgrading to an NKP Ultimate license, ensure the Control Plane nodes of the Management cluster are sized appropriately (e.g., sufficient CPU, memory, and storage) to support the additional default platform services enabled by the license.” For example, Control Plane nodes might need to be scaled to at least 4 vCPUs and 16 GB RAM, depending on the workload.

Incorrect Options:

A. Size the Sidecar containers appropriately: Sidecar containers are typically used for specific workloads (e.g., logging agents), not for platform services deployment, and are not a licensing prerequisite.

B. Size the ETCD nodes appropriately: While etcd is part of the Control Plane, the course refers to the Control Plane nodes as a whole, not etcd specifically, as the sizing requirement.

D. Size the Worker nodes appropriately: Worker nodes run user workloads, not the platform services, which are deployed on the Control Plane in the Management cluster.

Continuous Deployment (CD) is the practice of automatically deploying new software releases across environments after a successful build and testing phase. In the context of NKP, enabling CD ensures that software changes are consistently and reliably rolled out to all clusters associated with a project, ensuring operational uniformity and rapid feature adoption.

For creating custom images for vSphere (and other supported platforms), Konvoy Image Builder (KIB) is the officially supported tool for NKP. It can create images with custom configurations, package versions, and pre-bundled artifacts.

Key Reference:

“Konvoy Image Builder (KIB) is the Nutanix-recommended tool for building NKP cluster node images for vSphere, AWS, Azure, and other environments.”

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 explains that NKP uses Grafana Loki as part of its logging stack to implement a multi-tenant logging system, allowing teams to access their logs securely within their respective namespaces. The exhibit indicates two namespaces, tenant-innovation and tenant-analytics, each with a ConfigMap for logging configuration. The requirement is to set a retention period of 30 days for tenant-innovation and 7 days for tenant-analytics.

The correct YAML output (Option A) configures Loki’s retention period using ConfigMaps in the appropriate namespaces:

For tenant-innovation, the ConfigMap logging-innovation-config in the tenant-innovation namespace sets retention_period: 30d.

For tenant-analytics, the ConfigMap logging-analytics-config in the tenant-analytics namespace sets retention_period: 7d.

The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “In NKP, configure multi-tenant logging with Grafana Loki by creating a ConfigMap per namespace, specifying retention periods under loki.structuredConfig.limits_config.retention_period (e.g., 30d for 30 days, 7d for 7 days).” The d suffix denotes days, aligning with the requirement for 30 days and 7 days retention periods.

Incorrect Options:

B: The second ConfigMap incorrectly uses the tenant-innovation namespace instead of tenant-analytics, breaking the multi-tenant isolation.

C: The retention periods are set to 30h and 7h (hours), not 30d and 7d (days), which does not meet the requirement.

D: Both ConfigMaps use a generic tenant namespace, which does not match the specific namespaces (tenant-innovation, tenant-analytics) shown in the exhibit.

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.”

As stated in the NKPA 6.10 documentation, when using external storage (such as AWS S3) with Loki for log storage, AWS credentials must be provided securely. This typically involves creating a Kubernetes Secret containing the static AWS credentials (access key ID and secret access key), which are referenced in the override ConfigMap to authenticate Loki’s S3 storage backend.

Key reference from documentation:

“For Loki to store logs in an S3 bucket, AWS credentials must be created as a Kubernetes secret and referenced in the storage configuration.”

When mirroring the NKP bundle to a private registry, the recommended method is to use the nkp bundle push or similar commands with the --to-registry option, specifying the target registry along with the credentials (--to-registry-username and --to-registry-password).

Key reference from documentation:

“Use the --to-registry flag along with --to-registry-username and --to-registry-password to push the NKP bundle to a private registry.”

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.

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.

The NKPA 6.10 documentation confirms that workspaces in NKP are used to logically group and standardize configurations across multiple clusters. By creating a production workspace and a development workspace, the organization can control and enforce standardized configurations and policies for all relevant clusters.

Exact extract:

“Workspaces provide logical separation for resources, configuration, and policy management across multiple clusters, enabling standardized operations.”

The NKPA course outlines the default behaviors of NKP when deploying workload clusters within a workspace, regardless of the provisioning environment (e.g., AWS, Nutanix, Azure). When a workload cluster is deployed in AWS, NKP ensures consistency across the workspace by applying the following default behaviors:

The NKP workload cluster will receive all of the GitOps sources that have been assigned to the NKP workspace (Option A): NKP uses GitOps with Flux to manage cluster configurations and applications. The course explains that GitOps sources (e.g., Git repositories) assigned to a workspace are automatically applied to all clusters in that workspace, ensuring consistent configuration and application deployment. The Nutanix Cloud Native (NCP-CN) 6.10 Study Guide states: “New workload clusters inherit all GitOps sources configured for the workspace, enabling Flux to synchronize configurations from the specified repositories.”

The NKP workload cluster will be deployed all of the applications that have been enabled on this NKP workspace (Option D): NKP Platform Applications (e.g., Prometheus, Rook Ceph) enabled in the workspace are automatically deployed to new workload clusters. The NKPA course notes: “When a workload cluster is created, NKP deploys all platform applications enabled in the workspace to ensure consistent functionality across clusters.”

Incorrect Options:

B. The NKP workload cluster will receive all of the NKP RBAC policy: RBAC policies are defined at the workspace or project level and applied to users or groups, not automatically to clusters. Clusters inherit RBAC through user access, not as a default deployment behavior.

C. The NKP workload cluster will also be assigned to all of the NKP projects: Clusters are assigned to specific projects manually, not automatically to all projects in a workspace.