Nutanix Certified Professional – Artificial Intelligence (NCP-AI)

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The Nutanix Certified Professional – Artificial Intelligence (NCP-AI) certification validates a candidate’s ability to deploy, configure, operate, and troubleshoot enterprise-grade AI environments using Nutanix technologies. The exam focuses on real-world operational skills required to support machine learning, deep learning, and generative AI workloads on Nutanix infrastructure. It emphasizes hands-on knowledge of AI platforms, GPU-enabled systems, and lifecycle management of AI workloads in hybrid and cloud environments.

Exam Overview

The NCP-AI exam is designed to assess practical expertise in managing AI platforms rather than theoretical data science concepts. Candidates are tested on their ability to install and configure AI environments, manage compute and storage resources, optimize performance, monitor AI workloads, and resolve operational issues. The exam uses scenario-based multiple-choice and multiple-select questions to evaluate decision-making skills commonly required in enterprise AI operations.

Ideal For (Who Should Take This Exam)

This certification is ideal for IT professionals who work with AI infrastructure and enterprise platforms, including:

• AI or ML platform engineers
• Cloud and data center administrators supporting AI workloads
• DevOps and MLOps engineers
• Infrastructure engineers managing GPU-based environments
• Professionals responsible for deploying and maintaining AI solutions on Nutanix
  
  

It is especially suitable for candidates who already have experience with virtualization, Linux systems, containers, Kubernetes, and cloud-native infrastructure concepts.

Knowledge Areas CoveredAI Platform Deployment and Configuration

Covers installation and initial setup of Nutanix AI environments, including validating prerequisites, configuring system components, and integrating AI services with existing infrastructure.

Compute, Storage, and GPU Management

Focuses on managing high-performance compute resources, GPU allocation, storage optimization, and scaling AI workloads efficiently across clusters.

Data Pipelines and Model Lifecycle Management

Includes understanding how data flows through AI systems, managing datasets, deploying models, handling version control, and supporting retraining processes.

Monitoring and Performance Optimization

Addresses monitoring AI workloads, analyzing resource utilization, identifying bottlenecks, and tuning performance for training and inference workloads.

Security, Access Control, and Resilience

Covers role-based access control, data protection, backup strategies, and maintaining availability and reliability of AI platforms.

Troubleshooting and Operational Support

Focuses on identifying configuration issues, resolving performance problems, and maintaining stable AI environments in production.