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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Software Architectures	18%	- 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 - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
AI Overview	15%	- 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 Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Algorithm Types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - AI Deployment Models 1. On-premises 2. Cloud 3. Edge 4. Benefits and risks of each model - AI Industry Applications 1. Healthcare applications 2. Digital twins 3. Agents
AI Common Challenges	22%	- Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Maximizing performance in demanding AI workloads 3. Optimizing data access and movement - Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage
AI Lifecycle	27%	- Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - Data Preparation 1. XCP and CopySync 2. NetApp BlueXP Classification 3. Data aggregation and cleansing - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Distinction between predictive and generative AI 3. Impact of generative content (text, images, video, decision-making) - Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows
AI Hardware Architectures	18%	- 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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
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. NetApp FabricPool
B. A faster CPU in the server
C. NetApp SnapMirror
D. GPUDirect Storage

Answer: D

NEW QUESTION # 97
Which of the following describes the impact of generative AI in content creation?

A. Generative AI predicts customer preferences without creating new content.
B. Generative AI analyzes data without generating content.
C. Generative AI can create new content such as text, images, and videos.
D. Generative AI only processes pre-existing content.

Answer: C

NEW QUESTION # 98
An architect is designing a cost-optimized storage solution for a large AI environment that has two distinct data temperature profiles:
1. Hot Data (200 TB): Actively used training sets and models requiring all-flash performance.
2. Cold Data (2 PB): Inactive, archived datasets and old model versions that must remain online but are infrequently accessed.
The solution must automatically manage data placement based on activity, without requiring manual intervention or changes to file paths. The environment consists of an on-premises data center and a public cloud account.
Which combination of NetApp technologies provides the most complete and cost-effective solution? (Select all that apply.)

A. Deploy NetApp StorageGRID on-premises to serve as a cost-effective capacity tier for the cold data.
B. Use NetApp SnapMirror to create a full copy of the 2 PB of cold data on the AFF A-Series for faster access if needed.
C. Deploy a single, large Cloud Volumes ONTAP instance to hold both the hot and cold data tiers.
D. Use NetApp XCP to manually script the daily movement of cold files from the AFF system to StorageGRID.
E. Deploy a NetApp AFF A-Series system on-premises to serve as the high-performance tier for the hot data.
F. Use NetApp FabricPool to automatically and transparently tier cold data blocks from the AFF A- Series to the StorageGRID system.

Answer: A,E,F

NEW QUESTION # 99
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A. NetApp StorageGRID as the primary storage for the training datasets.
B. An InfiniBand or RoCE-capable Ethernet network fabric.
C. GPUDirect Storage support on the storage system.
D. A tiered storage architecture using NetApp FabricPool.
E. A 10GbE Ethernet network for all data traffic.

Answer: B,C

NEW QUESTION # 100
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?

A. A predictive AI model to classify legal documents.
B. A fine-tuning pipeline to update the LLM daily.
C. A Retrieval-Augmented Generation (RAG) architecture.
D. A new LLM trained from scratch on the legal documents.

Answer: C

NEW QUESTION # 101
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