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Which Nutanix product provides managed inference endpoints for foundation models running on AHV or ESXi clusters?

A
B
C
D
to track
2026 Statistics

Key Facts: Nutanix NCP-AI Exam

~75

Exam Questions

Nutanix

3000/6000

Passing Score

Nutanix

120 min

Exam Duration

Nutanix

$199

Exam Fee

Nutanix

60-100 hrs

Study Time

Recommended

2 years

Cert Valid

Nutanix

The NCP-AI exam has approximately 75 questions in 120 minutes with a 3000/6000 scaled passing score. It covers Nutanix Enterprise AI (NAI), NIM-based model serving, GPU passthrough and vGPU on AHV, NKP, Files/Objects/Volumes for AI data, RAG with vector databases, security, monitoring, and the LCM-driven lifecycle.

Sample Nutanix NCP-AI Practice Questions

Try these sample questions to test your Nutanix NCP-AI exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which Nutanix product provides managed inference endpoints for foundation models running on AHV or ESXi clusters?
A.Nutanix Era
B.Nutanix Enterprise AI (NAI)
C.Nutanix Files
D.Nutanix Calm
Explanation: Nutanix Enterprise AI (NAI) is the platform that delivers managed, secure inference endpoints for large language models on Nutanix clusters running AHV or ESXi, integrating with NVIDIA NIM containers and the Nutanix Kubernetes Platform.
2Which container runtime format does Nutanix Enterprise AI use to package and serve foundation models with GPU acceleration?
A.NVIDIA NIM (NVIDIA Inference Microservices)
B.Docker Swarm services
C.OpenShift Operators
D.AWS SageMaker containers
Explanation: Nutanix Enterprise AI uses NVIDIA NIM (NVIDIA Inference Microservices) containers to serve foundation models. NIM provides optimized, GPU-accelerated inference with standardized OpenAI-compatible APIs.
3Which Kubernetes distribution is the supported orchestration layer for Nutanix Enterprise AI workloads?
A.Amazon EKS
B.Rancher RKE2
C.Nutanix Kubernetes Platform (NKP)
D.Azure AKS
Explanation: Nutanix Kubernetes Platform (NKP), formerly known as Karbon, is the Nutanix-supported Kubernetes distribution that NAI uses to orchestrate model-serving pods, GPU scheduling, and lifecycle management on AHV.
4Which two GPU technologies enable workloads on AHV to access NVIDIA accelerators? (Choose the BEST single answer.)
A.GPU passthrough only
B.vGPU only via NVIDIA AI Enterprise
C.Both GPU passthrough and NVIDIA vGPU
D.Only SR-IOV based partitioning
Explanation: AHV supports both raw GPU passthrough (one GPU dedicated to one VM) and NVIDIA vGPU (a single physical GPU partitioned across multiple VMs) when licensed via NVIDIA AI Enterprise.
5Which Nutanix node families are typically certified to host H100, L40S, and A100 GPUs for AI workloads?
A.G5 series nodes
B.G7 series nodes
C.G8 and G9 series nodes
D.NX-1000 series only
Explanation: Modern G8 and G9 series Nutanix nodes provide the PCIe Gen4/Gen5 lanes, power, and thermal envelope required to host high-TDP GPUs such as the NVIDIA H100, L40S, and A100.
6An administrator must reserve specific GPU-equipped nodes exclusively for NAI inference pods. Which Kubernetes mechanism should they use?
A.Pod priority classes
B.Node taints with matching tolerations
C.Horizontal pod autoscaling
D.Service mesh routing rules
Explanation: Node taints applied to the GPU nodes, combined with matching tolerations on inference pods, prevent non-AI workloads from being scheduled on those nodes while allowing approved NAI pods to land there.
7Which Nutanix data service is BEST suited for storing large training datasets accessed concurrently over NFS by multiple GPU nodes?
A.Nutanix Volumes
B.Nutanix Files
C.Nutanix Objects
D.Nutanix Era
Explanation: Nutanix Files provides scale-out distributed file shares (NFS and SMB) that GPU nodes can mount concurrently for training datasets, with high parallel throughput and snapshot-based protection.
8Where does Nutanix recommend storing immutable model artifacts and checkpoints that must be retrieved by S3-compatible APIs?
A.Nutanix Volumes
B.Nutanix Files (NFS)
C.Nutanix Objects
D.Prism Central database
Explanation: Nutanix Objects is the S3-compatible object store designed for immutable artifacts such as model weights, checkpoints, and datasets, with versioning, WORM, and lifecycle policies.
9Which Nutanix storage service provides iSCSI block volumes ideal for hosting a high-performance vector database?
A.Nutanix Files
B.Nutanix Objects
C.Nutanix Volumes
D.Nutanix Move
Explanation: Nutanix Volumes presents iSCSI block storage that vector databases such as Milvus or pgvector can use as low-latency persistent volumes for embeddings indexes.
10Which open-source vector database is commonly deployed on Nutanix to power Retrieval Augmented Generation (RAG) pipelines?
A.Redis Cluster
B.Milvus
C.MongoDB Atlas
D.Cassandra
Explanation: Milvus is a purpose-built open-source vector database widely deployed on NKP-managed Kubernetes clusters as the embedding store for RAG workloads on Nutanix Enterprise AI.

About the Nutanix NCP-AI Exam

The Nutanix NCP-AI certification validates professional-level skills in deploying, securing, and operating Nutanix Enterprise AI (NAI) for foundation-model inference, including NIM containers, NVIDIA GPU integration on AHV, NKP, RAG architectures, vector databases, and lifecycle management with LCM.

Questions

75 scored questions

Time Limit

120 minutes

Passing Score

3000/6000 (scaled)

Exam Fee

$199 (Nutanix / Pearson VUE)

Nutanix NCP-AI Exam Content Outline

20%

Nutanix Enterprise AI Platform

NAI architecture, NIM containers, model catalog (Llama, Mistral, Hugging Face), endpoints, OpenAI-compatible APIs

20%

AI Infrastructure & GPU

AHV GPU passthrough, NVIDIA vGPU, NVAIE licensing, MIG, G8/G9 nodes with H100/L40S/A100, GPU scheduling, sizing

15%

Data Services for AI

Nutanix Files (NFS datasets), Objects (S3 model artifacts), Volumes (vector DB block), CSI driver, NKP storage classes

15%

Deployment & Lifecycle

LCM install/upgrade, NIM image import, endpoint creation, rolling upgrades, autoscaling, rollback, regression testing

10%

Security & Multi-Tenancy

API keys, RBAC, tenants and namespaces, Flow microsegmentation, TLS, audit logging, prompt logging policy, quotas

10%

Monitoring & Observability

Prism Central GPU dashboards, NCC checks, Prometheus/Grafana, latency metrics (TTFT, TPS), SIEM integration

10%

RAG & Use Cases

Vector databases (Milvus, Weaviate, pgvector), embedding models, document Q&A, summarization, code-gen, chat assistants

How to Pass the Nutanix NCP-AI Exam

What You Need to Know

  • Passing score: 3000/6000 (scaled)
  • Exam length: 75 questions
  • Time limit: 120 minutes
  • Exam fee: $199

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Nutanix NCP-AI Study Tips from Top Performers

1Build a small NAI lab: deploy NKP on AHV, install NAI via LCM, import a Llama or Mistral NIM image, and create an endpoint
2Master GPU concepts: passthrough vs vGPU vs MIG, NVAIE licensing, GPU Operator on NKP, and node taints/labels for scheduling
3Practice the data services map: Files for NFS datasets, Objects for S3 model artifacts, Volumes for vector DB block storage
4Stand up a real RAG pipeline with Milvus, pgvector, or Weaviate to internalize embeddings, retrieval, and prompt grounding
5Drill the LCM workflow: inventory, dependency order (AOS, NKP, NAI), rolling upgrades of endpoints, and rollback paths
6Memorize security defaults: API keys per endpoint, tenant/namespace isolation, Flow microsegmentation, TLS, audit logging
7Wire up Prometheus and Grafana to your NAI endpoints; learn TTFT, tokens-per-second, GPU utilization, and queue depth as core metrics

Frequently Asked Questions

What is the NCP-AI passing score and format?

Nutanix uses a 3000 out of 6000 scaled score. The exam has approximately 75 multiple-choice questions delivered in a 2-hour window via Pearson VUE testing centers or online proctoring.

Do I need NCP-MCI before taking NCP-AI?

NCP-MCI is recommended but not strictly required. You should already understand AOS, AHV, Prism Central, and Nutanix Files/Objects basics before adding the NAI, NIM, and GPU-specific content of NCP-AI.

How long should I study?

Most candidates invest 60-100 hours over 6-10 weeks. Hands-on time with NAI, NIM endpoints, NKP, GPU-equipped nodes, and a RAG pipeline using Milvus or pgvector is essential.

What topics carry the most weight?

The NAI platform itself (NIM, model catalog, endpoints) and AI infrastructure (GPU passthrough, vGPU, NVAIE, sizing) together cover roughly 40% of questions, followed by data services, lifecycle, security, monitoring, and RAG.

Which GPUs and node families are emphasized?

Modern G8 and G9 Nutanix nodes hosting NVIDIA H100, L40S, and A100 GPUs, configured with NVIDIA AI Enterprise licensing for vGPU plus the NVIDIA GPU Operator on NKP worker nodes.

What is the certification validity period?

Nutanix professional certifications are valid for 2 years. Renewal is typically achieved by passing a current version of the exam or an upgrade exam before expiration.

How much does NCP-AI cost?

The exam fee is approximately US$199 through Pearson VUE. Retake fees match the original exam fee, and Nutanix may publish promotional vouchers periodically through Nutanix University.