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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

Answer: A,B


NEW QUESTION # 67
An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

Answer: A


NEW QUESTION # 68
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?

Answer: D


NEW QUESTION # 69
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)

Answer: A,C,D,E


NEW QUESTION # 70
A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

Answer: B


NEW QUESTION # 71
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