Free Practice Questions for Nutanix NCP-AI Exam

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

The Nutanix Enterprise AI Dashboard includes a specific widget designed to provide granular visibility into credential usage patterns. The API Keys (Top 5) widget displays a ranked list of the specific API keys that are driving the most traffic to the inference endpoints.

While the "API Requests Trends" widget shows the total volume of requests over time, theAPI Keys (Top 5)widget breaks this data down by credential, allowing the administrator to instantly identify which specific users or applications ("busiest credentials") are generating the highest load on the system.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

The symptoms described point directly to amemory bottleneckaffecting the performance of the AI training job.

Analysis:"Memory ballooning is active" and "swap usage is increasing" indicate that the Virtual Machine (VM) does not have enough physical RAM to hold the working set of the data processing job. When the OS is forced to swap memory pages to disk, performance degrades severely (stalling), even if the GPU is capable of processing data quickly (stable 85% utilization).

Solution:Increasing the VM's RAMensures that the entire dataset or batch can remain in high-speed physical memory. This eliminates the need for ballooning (reclaiming memory from the guest) and swapping to disk, allowing the CPU and GPU to be fed data continuously without I/O wait times.

Other Options:

Reducing vCPUs (A) won't solve the memory shortage.

Adding vGPUs (B) won't help if the system cannot feed data to the existing GPU fast enough due to swapping.

Disabling passthrough (D) typically lowers performance for heavy training jobs and does not address the memory capacity issue.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

According to the Role-Based Access Control (RBAC) definitions within the Nutanix Enterprise AI (NAI) platform, there is a strict segregation of resources to ensure privacy and security in multi-tenant environments.

User Isolation:While anAI/ML Userhas the privileges to create, manage, and delete theirownendpoints and generate API keys for applications they are developing, they do not have global visibility into the private resources of others.

Model Visibility:An AI/ML User can view and deployPre-validated Models(provided globally by the Administrator) orCustom Modelsthat they have imported themselves (if the "Allow Manual Upload" permission is granted).1However, theycannot viewCustom Models that have been imported by other AI/ML Users. These models are scoped to the user who created them to preventing unauthorized access to proprietary or sensitive model weights uploaded by different teams.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

According to the Nutanix Enterprise AI documentation regarding "Viewing Sample API Code," the sample code provided by the dashboard is designed to help developers integrate the endpoint into their applications. The documentation explicitly states: "The sample code contains the URL and API key required to access the endpoint."

These two components are essential for the client application to locate the service (Endpoint URL) and authenticate the request (API Key). User credentials (such as username/password) are not part of the standard inference API request, and infrastructure details like GPU type are abstracted away from the client code.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

Nutanix Enterprise AI defines specific "Deployment Types" based on the underlying infrastructure platform.

Standalone Deployment:This deployment type is explicitly defined in the documentation as the method for deploying Nutanix Enterprise AI Pro on "any Kubernetes cluster running on bare metal servers, on-prem VMs, andpublic clouds such as Amazon EKS, Azure AKS, or Google GKE".

Differentiation:

GPT-in-a-Box 2.0refers to the full-stack on-premises solution including Nutanix Cloud Infrastructure (NCI) and Hardware.

Cloud Clusteris often a colloquial term or refers to Nutanix Cloud Clusters (NC2), but "Standalone" is the formal deployment SKU/type for bringing your own Kubernetes (BYOK8s) like EKS.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

The symptoms described—high CPU/memory saturation and increasing latency during peaks—indicate that the current inference infrastructure is underprovisioned for the load it is receiving.

The correct remediation in Nutanix Enterprise AI is to scale out the endpoint. By updating the endpoint configuration to increase the number of instances (replicas) or by allocating more compute resources (CPU/Memory per pod) if the model is constrained by the container size, the system can distribute the inference requests across more capacity. This parallelization directly addresses the bottleneck, lowering latency and preventing timeouts. Restarting is a temporary fix that does not solve the capacity issue, and API keys do not impact performance resources.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

In the Nutanix Enterprise AI (NAI) workflow, API keys are mechanism-specific credentials used to secure access to inference services.

Dependency:The documentation for creating API keys explicitly states that the key must be assigned to anEndpoint.

Workflow:When navigating to the "API Keys" section to generate a new key, the user is required to select an existing endpoint from a dropdown menu to associate the key with. Consequently, you cannot create a functional API key in a vacuum; you mustCreate an Endpoint first(which itself implies you have already imported a model) before you can generate the credentials to access it. The endpoint acts as the parent object that the key authenticates against.

Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:

When deploying Nutanix Enterprise AI or its underlying Kubernetes infrastructure on public cloud managed services, specific configurations apply regarding hardware acceleration.

Azure AKS Specifics:When configuring an Azure Kubernetes Service (AKS) cluster to support GPU workloads for NAI, the underlying Virtual Machine (VM) images provided by Azure for GPU instances (such as the NC or NV series) come pre-configured with the necessary NVIDIA drivers.

Installation Procedure:Consequently, the documentation explicitly states that when setting up the GPU nodes or configuring the GPU Operator on AKS, the administrator mustskip the step to install the GPU driver. Attempting to reinstall the driver over the pre-existing Azure driver can cause conflicts, instability, or deployment failures. The setup should rely on the cloud provider's pre-installed driver stack.

**Comprehensive and Detailed Explanation From Exact Extract of Nutanix Artificial Intelligence documents knowledge without any external URL or links:**

Nutanix Enterprise AI (NAI) is a cloud-native application that requires a Kubernetes layer to operate. The supported Nutanix-native Kubernetes platform for deploying NAI is the **Nutanix Kubernetes Engine (NKE)** (often managed via the Nutanix Kubernetes Platform, NKP).

NKE provides the certified CNCF-compliant Kubernetes clusters running on top of Nutanix Cloud Infrastructure (NCI) that NAI requires to orchestrate containers and manage GPU resources. While NAI can effectively run on public cloud Kubernetes services (like EKS or AKS), within the context of Nutanix-provided platforms, **Nutanix NKE** is the designated supported deployment platform for the Kubernetes layer. (Note: On-premises NCI is the underlying infrastructure, not the AI deployment platform itself).