NS0-901 Pass Test Guide & NS0-901 Exam Revision Plan

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

Answer: C


NEW QUESTION # 66
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

Answer: D,E


NEW QUESTION # 67
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

Answer: A,C


NEW QUESTION # 68
An architect is designing an AI solution for a European hospital chain to analyze patient diagnostic scans. The project is subject to strict GDPR regulations, which mandate that patient data cannot leave the sovereign territory. The application also requires near-instantaneous results for physicians reviewing the scans in the hospital.
Which deployment model best satisfies these security and performance requirements?

Answer: A


NEW QUESTION # 69
The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?

Answer: D


NEW QUESTION # 70
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