Network Appliance NetApp Certified AI Expert NS0-901 Certified Exam Dumps

NS0-901 Exam Dumps

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Certification Provider: Network Appliance
Exam Code / Number: NS0-901
Exam Name: NetApp Certified AI Expert Exam
Exam Questions: 106
Last Updated: Jun 24, 2026
Corresponding Certification: NetApp Certified AI Expert

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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Common Challenges22%- Traceability and Optimization
  • 1. Maximizing performance in demanding AI workloads
  • 2. Ensuring traceability for code, data, and models
  • 3. Optimizing data access and movement
- Resource Management
  • 1. Controlling costs and securing storage
  • 2. Sizing storage and compute resources effectively
Topic 2: AI Software Architectures18%- Development Tools
  • 1. Jupyter notebooks vs. pipelines
  • 2. NetApp DataOps Toolkit
- Scaling and Orchestration
  • 1. Scaling AI workloads with Kubernetes
  • 2. Leveraging BlueXP software tools
- MLOps and LLMOps Ecosystems
  • 1. Understanding the software tools and platforms enabling AI at scale
Topic 3: AI Lifecycle27%- Generative AI Concepts
  • 1. Retrieval Augmented Generation (RAG)
  • 2. Hallucinations
  • 3. Fine-tuning
- Model Development
  • 1. Inferencing
  • 2. Fine-tuning workflows
  • 3. Model building
- Data Preparation
  • 1. XCP and CopySync
  • 2. NetApp BlueXP Classification
  • 3. Data aggregation and cleansing
- Predictive AI vs. Generative AI
  • 1. Large Language Models (LLMs)
  • 2. Distinction between predictive and generative AI
  • 3. Impact of generative content (text, images, video, decision-making)
Topic 4: AI Overview15%- AI Industry Applications
  • 1. Agents
  • 2. Digital twins
  • 3. Healthcare applications
- Training vs. Inferencing vs. Predictions
  • 1. Distinguish between training and inference workloads
- AI Convergence with HPC and Analytics
  • 1. Leveraging shared infrastructure for AI, HPC, and analytics
- Algorithm Types
  • 1. Unsupervised learning
  • 2. Supervised learning
  • 3. Reinforcement learning
- AI Deployment Models
  • 1. On-premises
  • 2. Edge
  • 3. Cloud
  • 4. Benefits and risks of each model
- Machine Learning Fundamentals
  • 1. Understand the relationship between AI, machine learning, and deep learning
  • 2. Describe machine learning benefits
Topic 5: AI Hardware Architectures18%- Networking and Storage
  • 1. Storage architectures for AI
  • 2. Network protocols for AI workloads
- NetApp Architectures
  • 1. OVX architectures
  • 2. BasePod
  • 3. SuperPOD
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies


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