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AI+ Network Practitioner™

AI+ Network Practitioner™

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.

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Certificate Code

AT-510

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic networking, Python, AI/ML fundamentals, and familiarity with network management tools.
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  1. 1.1 Basic Networking Concepts
  2. 1.2 Network Infrastructure and Design
  3. 1.3 Introduction to Network Security
  4. 1.4 AI Workload Networking Overview

  1. 2.1 Advanced Routing and Switching
  2. 2.2 Data Center and AI Infrastructure Networking
  3. 2.3 High-Performance AI Fabric Considerations
  4. 2.4 Quality of Service (QoS) for Application and AI Workloads

  1. 3.1 Network Virtualization and Cloud Networking Models
  2. 3.2 SD-WAN and Hybrid Multi-Cloud Connectivity
  3. 3.3 SASE and SSE with AI

  1. 4.1 Wi-Fi 7 and AI-Driven RF Optimization
  2. 4.2 Edge Computing, Fog Networking and IoT Models
  3. 4.3 Edge AI and Small Language Models
  4. 4.4 Wi-Fi 7 + Edge AI Use Cases and Architecture

  1. 5.1 AI and Machine Learning Fundamentals
  2. 5.2 AI-Driven Network Optimization
  3. 5.3 Operational Limits of AI Recommendations
  4. 5.4 Predictive Network Maintenance

  1. 6.1 Generative AI and LLM Concepts for Network Operations
  2. 6.2 RAG (Retrieval-Augmented Generation) for Network Knowledge
  3. 6.3 Prompt Engineering for Network Engineers

  1. 7.1 Fundamentals of Network Automation & Infrastructure as Code (IaC)
  2. 7.2 Network APIs and Programmability
  3. 7.3 Agentic AI, Function Calling, and MCP
  4. 7.4 ChatOps and Operational Workflows
  5. 7.5 Use-Cases and Case Studies

  1. 8.1 AI-Enhanced Threat Detection
  2. 8.2 Secure Network Design and Zero Trust
  3. 8.3 SIEM, SOC, and AI-Assisted Security Operations
  4. 8.4 Adversarial AI and AI Security Risks
  5. 8.5 Use-Cases and Case Studies

  1. 9.1 Modern Observability Foundations (Metrics, Logs, and Traces)
  2. 9.2 eBPF for Deep Network Visibility
  3. 9.3 OpenTelemetry and Streaming Telemetry Standards
  4. 9.4 AIOps: Alert Correlation, Noise Reduction, and Root Cause Support
  5. 9.5 Use-Cases and Case Studies

  1. 10.1 AI Governance and Responsible Network Operations
  2. 10.2 Privacy, Data Handling, and Bias in Network AI
  3. 10.3 Sustainable/Green Networking with AI
  4. 10.4 Future Network Operations
  5. 10.5 Use-Cases and Case Studies

  1. 11.1 Capstone Objective
  2. 11.2 Capstone Scenario

  1. 1.1 What Are AI Agents
  2. 1.2 Applications and Trends of AI Agents in Network Intelligence
  3. 1.3 How Does an AI Agent Work
  4. 1.4 Characteristics of AI Agents
  5. 1.5 Types of AI Agents

AI Tools Covered

Ansible
Puppet
Chef
REST APIs
NETCONF
Kubernetes
OpenStack
GNS3
Cisco Packet Tracer
VMware
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