AI+ Network Practitioner™
Validate Your Expertise in Networking: Harness AI for Automation, Security, and Next-Generation Efficiency
$495one-time payment
Overview
About This Certification
This certification validates professional knowledge and competency in the combination of artificial intelligence and current networking technologies. The exam assesses understanding of fundamental networking concepts, newer technologies such as SDN and NFV, and how AI can enhance network efficiency. Key focus areas include AI-powered network automation, orchestration, and security upgrades. The exam includes scenario-based questions covering emerging developments in AI-enhanced networking, validating candidate readiness for leadership roles in this rapidly evolving sector.
Prerequisites & Duration
Basic networking, Python, AI/ML fundamentals, and familiarity with network management tools.
Instructor-Led: 5 days (live or virtual) Self-Paced: 40 hours of content
Assessment
50 questions, 70% passing, 90 minutes, online proctored exam
Curriculum
Certification Modules
Module 1: Enterprise Networking Foundations & AI Workload Impact
1.1 Basic Networking Concepts 1.2 Network Infrastructure and Design 1.3 Introduction to Network Security 1.4 AI Workload Networking Overview
Module 2: Advanced Routing, Switching, and Data Center/AI Fabric Networking
2.1 Advanced Routing and Switching 2.2 Data Center and AI Infrastructure Networking 2.3 High-Performance AI Fabric Considerations 2.4 Quality of Service (QoS) for Application and AI Workloads
Module 3: Cloud Networking, SASE, and Hybrid Connectivity
3.1 Network Virtualization and Cloud Networking Models 3.2 SD-WAN and Hybrid Multi-Cloud Connectivity 3.3 SASE and SSE with AI
Module 4: Wi-Fi 7, Edge AI & IoT Networking
4.1 Wi-Fi 7 and AI-Driven RF Optimization 4.2 Edge Computing, Fog Networking and IoT Models 4.3 Edge AI and Small Language Models 4.4 Wi-Fi 7 + Edge AI Use Cases and Architecture
Module 5: AI & Machine Learning Foundations for Network Engineers
5.1 AI and Machine Learning Fundamentals 5.2 AI-Driven Network Optimization 5.3 Operational Limits of AI Recommendations 5.4 Predictive Network Maintenance
Module 6: Generative AI, RAG, and Prompt Engineering for Operations
6.1 Generative AI and LLM Concepts for Network Operations 6.2 RAG (Retrieval-Augmented Generation) for Network Knowledge 6.3 Prompt Engineering for Network Engineers
Module 7: Network Automation, IaC, and Agentic AI Workflows
7.1 Fundamentals of Network Automation & Infrastructure as Code (IaC) 7.2 Network APIs and Programmability 7.3 Agentic AI, Function Calling, and MCP 7.4 ChatOps and Operational Workflows 7.5 Use-Cases and Case Studies
Module 8: AI-Enhanced Network Security and Zero Trust
8.1 AI-Enhanced Threat Detection 8.2 Secure Network Design and Zero Trust 8.3 SIEM, SOC, and AI-Assisted Security Operations 8.4 Adversarial AI and AI Security Risks 8.5 Use-Cases and Case Studies
Module 9: Modern Observability: eBPF, OpenTelemetry, and AIOps
9.1 Modern Observability Foundations (Metrics, Logs, and Traces) 9.2 eBPF for Deep Network Visibility 9.3 OpenTelemetry and Streaming Telemetry Standards 9.4 AIOps: Alert Correlation, Noise Reduction, and Root Cause Support 9.5 Use-Cases and Case Studies
Module 10: AI Governance, Responsible AI, and Sustainable Networking
10.1 AI Governance and Responsible Network Operations 10.2 Privacy, Data Handling, and Bias in Network AI 10.3 Sustainable/Green Networking with AI 10.4 Future Network Operations 10.5 Use-Cases and Case Studies
Module 11: Capstone Project - End-to-End AI Network Operations
11.1 Capstone Objective 11.2 Capstone Scenario
Optional Module: Optional Module: AI Agents For Network
1.1 What Are AI Agents 1.2 Applications and Trends of AI Agents in Network Intelligence 1.3 How Does an AI Agent Work 1.4 Characteristics of AI Agents 1.5 Types of AI Agents
Tools
AI Tools Covered
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