NS0-901 NetApp Certified AI Expert Exam Knowledge Points Directory

This page serves as a structured knowledge point directory organized according to the official exam objectives.

Each topic below links to a dedicated learning page designed to support topic-based study, technical understanding, and certification exam preparation.

AI Overview

1. What is Artificial Intelligence?

2. Subtypes of AI

3. Relationship with Machine Learning (ML) and Deep Learning (DL)

4. AI Use Cases

5. AI Infrastructure Basics

AI Lifecycle

What is the AI Lifecycle?

1. Stages of the AI Lifecycle

2. MLOps (Machine Learning Operations)

3. AI Deployment Strategies

AI Software Architectures

1. Core Components

2. Experiment Tracking

3. Containerization and Orchestration

4. CI/CD in AI (Continuous Integration / Continuous Deployment)

5. Common Frameworks

AI Hardware Architectures

1. Compute Layer

2. Storage Layer

3. Network Layer

4. Hardware Utilization Techniques

AI Common Challenges

1. Data-Related Challenges

2. Model-Related Challenges

3. Operational Challenges

4. Ethical and Security Challenges

AI Overview (Additional Content)

1. AI Modes: Training vs Inference

2. AI Deployment Models: Cloud vs On-Premises vs Edge

3. Integration of AI with HPC and Big Data Analytics

4. Current Challenges in Modern AI Architectures

AI Lifecycle (Additional Content)

1. Training vs Inference: Functional and Infrastructure Differences

2. Pre-training and Fine-tuning in Model Development

3. Infrastructure Dependencies in Model Deployment and Monitoring

4. Common Tools and Platforms in the AI Lifecycle

AI Software Architectures (Additional Content)

1. Notebook vs Pipeline Architectures

2. Model Registry and Model Management

3. AI Software Architecture and NetApp Tools Integration

4. Multi-Framework Model Support and ONNX Optimization

AI Hardware Architectures (Additional Content)

1. Architecture Integration Examples: SuperPOD and FlexPod AI

2. Data Aggregation Structures in AI Architectures

3. Storage System Performance Metrics

4. AI Cluster Resource Scheduling and GPU Management

AI Common Challenges (Additional Content)

1. Data Drift vs Concept Drift

2. Regulatory Compliance Examples

3. Open-Source Tools for AI Security