Study with Network Appliance : NS0-901 Exam Torrent as your best preparation materials

Last Updated: Aug 05, 2026

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Network Appliance NS0-901 Exam Syllabus Topics:

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

Network Appliance NetApp Certified AI Expert Sample Questions:

1. To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A) NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
B) NetApp SnapLock to make the source data immutable before scanning.
C) A custom "PII" category within BlueXP classification to identify specific medical record number formats.
D) A manual review process where technicians visually inspect each scan for PII.
E) A custom Python script that uses regular expressions to search file contents.


2. Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A) RunAI
B) All of the above
C) Domino Data Labs
D) Google VertexAI


3. What is the primary difference between C-Series and A-Series storage for AI workloads?

A) A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
B) C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
C) C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
D) A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.


4. An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?

A) GPUDirect Storage
B) NetApp SnapMirror
C) A faster CPU in the server
D) NetApp FabricPool


5. A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?

A) The 'vsadmin' role does not have permission to delete Snapshots.
B) Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.
C) The Snapshot is locked by a SnapMirror relationship.
D) The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.


Solutions:

Question # 1
Answer: A,C
Question # 2
Answer: B
Question # 3
Answer: C
Question # 4
Answer: A
Question # 5
Answer: B

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