AI+ Network Practitioner™
AT-510
Validate Your Expertise in Networking: Harness AI for Automation, Security, and Next-Generation EfficiencyThis 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.
Why This Certification Matters
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.
At a Glance: Course + Exam Overview
- Instructor-Led: 5 days (live or virtual)
- Self-Paced: 40 hours of content
Job Roles & Industry Outlook
Cyber Security Analyst
Protect digital assets by identifying vulnerabilities, monitoring threats, and applying AI to strengthen security defenses.
Security Compliance Analyst
Keep organizations audit-ready by using AI to track policy gaps and regulatory risk before an auditor finds them.
Ethical Hacker / Penetration Testers
Break into systems on purpose, using AI-assisted tools to find vulnerabilities before real attackers do.
Business Analyst
Analyze business processes and ensure AI initiatives comply with legal, ethical, and regulatory standards.
Network Administrator
Keep networks running and secure by using AI to catch anomalies and automate routine maintenance before they become outages.
Data Analyst / Engineer
Turn raw data into reliable pipelines and clear insights that help teams make faster, better-informed decisions.
Industry Growth: AI+ Network Practitioner™
- Comprehensive Knowledge Covers fundamental networking concepts and advanced AI-driven technologies like SDN and NFV.
- AI-Powered Efficiency Exam assesses knowledge of how AI can optimize network performance, automation, and security
- Practical Application Assessment Exam includes scenario-based questions for real-world application of AI in networking.
- Future-Ready Skills Validates competency to adapt to the rapidly evolving AI-enhanced networking landscape.
- Leadership Opportunities Validates competency for leadership roles in the AI and networking fields.
Who Should Enroll?
- Networking Professionals: Looking to advance your skills by integrating AI into network design, automation, and security to stay ahead in a competitive field.
- AI Enthusiasts: Interested in applying AI technologies like ML and automation Specifically, networking domains.
- IT Specialists: Focused on exploring the intersection of cloud computing, IoT, and AI to optimize infrastructure and network performance.
- Students and Fresh Graduates: Pursuing careers in AI, cybersecurity, or networking and wanting to gain hands-on experience and credentials to strengthen their professional profile.
- Cybersecurity Analysts: Seeking to utilize AI-driven solutions for threat detection, network protection, and predictive analytics.
- System Administrators: Eager to transition into roles involving network automation, orchestration, and AI-based tools for managing complex systems.
- Tech Innovators and Researchers: Interested in exploring emerging trends like 5G, edge computing, or blockchain in AI-enhanced networking.
What You'll Learn
- 1.1 Basic Networking Concepts
- 1.2 Network Infrastructure and Design
- 1.3 Introduction to Network Security
- 1.4 AI Workload Networking Overview
- 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
- 3.1 Network Virtualization and Cloud Networking Models
- 3.2 SD-WAN and Hybrid Multi-Cloud Connectivity
- 3.3 SASE and SSE with AI
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 11.1 Capstone Objective
- 11.2 Capstone Scenario
- 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 You'll Explore
Ansible
Puppet
Chef
REST APIs
NETCONF
Kubernetes
OpenStack
GNS3
Cisco Packet Tracer
VMware
Prerequisites
- Basic networking, Python, AI/ML fundamentals, and familiarity with network management tools.
Exam Details
Duration
90 minutes
Passing Score
70% (35/50)
Format
50 multiple-choice/multiple-response questions
Delivery Method
Online via AI proctored exam platform (flexible scheduling)
Exam Blueprint
- Module 1: Enterprise Networking Foundations & AI Workload Impact - 8%
- Module 2: Advanced Routing, Switching, and Data Center/AI Fabric Networking - 8%
- Module 3: Cloud Networking, SASE, and Hybrid Connectivity - 8%
- Module 4: Wi-Fi 7, Edge AI & IoT Networking - 8%
- Module 5: AI & Machine Learning Foundations for Network Engineers - 8%
- Module 6: Generative AI, RAG, and Prompt Engineering for Operations - 8%
- Module 7: Network Automation, IaC, and Agentic AI Workflows - 8%
- Module 8: AI-Enhanced Network Security and Zero Trust - 8%
- Module 9: Modern Observability: eBPF, OpenTelemetry, and AIOps - 9%
- Module 10: AI Governance, Responsible AI, and Sustainable Networking - 9%
- Module 11: Capstone Project - End-to-End AI Network Operations - 9%
- Optional Module: Optional Module: AI Agents For Network - 9%
Choose the Format That Fits Your Schedule
What's Included (One-Year Subscription + All Updates):
- High-Quality Videos, E-book (PDF & Audio), and Podcasts
- AI Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Online Proctored Exam with One Free Retake
- Comprehensive Exam Study Guide
- Access for Tablet & Phone
Frequently Asked Questions
The course includes a mix of theoretical knowledge and practical applications, culminating in an interactive capstone project. This structure ensures that participants gain both conceptual understanding and hands-on experience.
This course is ideal for developers, IT professionals, and anyone with a foundational understanding of AI and cloud computing who wants to enhance their skills in integrating AI with cloud platforms like AWS, Azure, or Google Cloud.
Participants will learn to develop, deploy, and manage AI models on leading cloud platforms. Skills include optimizing AI model performance, ensuring security, meeting compliance standards, and applying AI and cloud concepts to solve real-world problems.
This certification enhances your professional profile by demonstrating proficiency in integrating AI with cloud computing. It equips you with in-demand skills, giving you a competitive edge in the job market and opening doors to lucrative career opportunities.
The certification includes an interactive capstone project where participants apply their knowledge to design and implement AI solutions within cloud environments. This project is designed to simulate real-world scenarios and challenges.