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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
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. Reinforcement learning
  • 2. Supervised learning
  • 3. Unsupervised learning
- AI Deployment Models
  • 1. Cloud
  • 2. Benefits and risks of each model
  • 3. Edge
  • 4. On-premises
- Machine Learning Fundamentals
  • 1. Describe machine learning benefits
  • 2. Understand the relationship between AI, machine learning, and deep learning
AI Hardware Architectures18%- NetApp Architectures
  • 1. OVX architectures
  • 2. BasePod
  • 3. SuperPOD
- Networking and Storage
  • 1. Network protocols for AI workloads
  • 2. Storage architectures for AI
- Infrastructure Topologies
  • 1. Data aggregation and compute topologies
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. Optimizing data access and movement
  • 3. Maximizing performance in demanding AI workloads
AI Software Architectures18%- 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
- Development Tools
  • 1. NetApp DataOps Toolkit
  • 2. Jupyter notebooks vs. pipelines
AI Lifecycle27%- Predictive AI vs. Generative AI
  • 1. Impact of generative content (text, images, video, decision-making)
  • 2. Distinction between predictive and generative AI
  • 3. Large Language Models (LLMs)
- Model Development
  • 1. Inferencing
  • 2. Model building
  • 3. Fine-tuning workflows
- Generative AI Concepts
  • 1. Fine-tuning
  • 2. Hallucinations
  • 3. Retrieval Augmented Generation (RAG)
- Data Preparation
  • 1. Data aggregation and cleansing
  • 2. NetApp BlueXP Classification
  • 3. XCP and CopySync

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
An AI team is planning two separate projects. The architect needs to provision the appropriate infrastructure for each.
| | Project A | Project B|
| -- | | - |
| Goal | Build a novel image recognition model from scratch.
| Adapt an existing, pre- trained LLM to understand company-specific jargon. |
| Input Data | 10 million new, unlabeled images. | A 50 GB text corpus of internal documents. |
| Required Compute | Very High (Weeks of multi-GPU training) | Moderate (Hours of single-GPU training) | Which two statements accurately describe the infrastructure requirements for these projects?
(Choose two.)

Answer: C,D


NEW QUESTION # 32
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: C


NEW QUESTION # 33
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)

Answer: A,B


NEW QUESTION # 34
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

Answer: C


NEW QUESTION # 35
The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

Answer: A


NEW QUESTION # 36
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