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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

Answer: B


NEW QUESTION # 44
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

Answer: B,E


NEW QUESTION # 45
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 # 46
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 # 47
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

Answer: D


NEW QUESTION # 48
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