Network Appliance NS0-901 Exam Details & Actual Exam Questions
- Exam Code/Number: NS0-901
- Exam Name/Title: NetApp Certified AI Expert Exam
- Certification Provider: Network Appliance
- Corresponding Certification: NetApp Certified AI Expert
- Exam Questions: 106
- Updated On: Jun,29 2026
- Certification Level: Expert
Network Appliance NetApp Certified AI Expert Exam Questions
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(20 Up Votes)Network Appliance NS0-901 Exam Overview:
| Certification Vendor: | NetApp (Network Appliance) |
| Exam Name: | NetApp Certified AI Expert Exam |
| Exam Number: | NS0-901 |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple-choice, Scenario-based questions |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Passing Score: | 66% |
| Real Exam Qty: | 60 |
| Exam Price: | 250 USD |
| Sample Questions: | Network Appliance NS0-901 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | 6–12 months of technical experience with AI workloads; knowledge of NetApp ONTAP, AI frameworks, and data workflows |
| Official Syllabus URL: | https://www.netapp.com/support-and-training/netapp-learning-services/certifications/ai-expert/ |
Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Lifecycle | 27% | - AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI - Model training, inference, and optimization - Predictive vs generative AI - AI governance, ethics, and compliance |
| Topic 2: NetApp AI Solutions and Architecture | 25% | - Storage architectures for AI workloads - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Data management and data pipeline design - Scalability and performance optimization for AI |
| Topic 3: AI Overview | 15% | - AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts |
| Topic 4: Security, Reliability, and Operations | 15% | - Cost management and efficiency - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - High availability and data protection |
| Topic 5: Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures |
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