AI in Cisco Certifications: Elevate Your Expertise

Introduction

Imagine walking into a technical interview for a senior network engineering role and being asked not about OSPF route redistribution or spanning-tree root guard, but about how you would leverage machine learning models to predict network failures before they happen. That scenario is no longer hypothetical. The AI Cisco certification landscape has fundamentally shifted, and professionals who ignore this transformation risk being left behind. With global spending on AI projected to reach $300 billion by 2026 and an estimated $15.7 trillion potential contribution to the global economy by 2030, artificial intelligence is no longer a niche specialty reserved for data scientists. It has become an essential competency for every IT professional, including those whose careers are built on routers, switches, and firewalls.

This article provides a comprehensive deep dive into how AI is being woven into Cisco certification exams, what foundational and advanced AI skills you need to master, how job roles for network engineers are shifting in response to AI, and what the infrastructure behind AI workloads actually looks like. Whether you are preparing for the CCNA, pursuing CCNP-level specializations, or targeting the expert-level CCDE, this guide will help you understand exactly what to study and why it matters for your career.

Why AI Is Changing Everything in Networking

The integration of AI into networking is not a marketing trend or a passing phase. It represents a fundamental shift in how infrastructure is designed, deployed, monitored, and secured. To understand why AI now appears prominently in Cisco certification exams, you need to appreciate the scale of the transformation happening across every industry.

The Economic Scale of AI Adoption

The numbers tell a compelling story. According to industry research, 75% of large enterprises will rely on AI-infused processes by 2026. AI is no longer confined to research laboratories or Silicon Valley startups. It is being deployed across virtually every sector:

Industry	AI Applications
Healthcare and Life Sciences	Diagnosis, drug discovery, personalized medicine
Agriculture	Yield optimization, automated irrigation, pest prediction and prevention
Retail	Personalization, inventory optimization, virtual agents
Energy	Distribution optimization, fault prediction, demand forecasting
Financial Services	Fraud detection, risk assessment, trading
Transportation	Route optimization, autonomous vehicles, predictive maintenance
Manufacturing	Predictive maintenance, quality control, demand forecasting
Public Sector	Smart cities, security, services improvement

Every one of these use cases depends on robust, high-performance network infrastructure. The network engineer who understands AI is not just more employable --- they are essential to making these deployments actually work.

The Data Explosion Driving AI

One of the most striking statistics in the AI landscape is that 90% of the world's data was generated in the last two years. This explosion in data volume is precisely what makes modern AI possible. Traditional rule-based programming --- the old "if-then-else" approach --- simply cannot scale to handle this volume. Machines needed to learn from data on their own, and that is exactly what modern machine learning enables.

Technology is now defining every aspect of our lives across three critical pillars: infrastructure, cybersecurity, and AI and data. For networking professionals, all three pillars converge in your domain. Your infrastructure carries AI workloads. Your security posture must protect AI systems while leveraging AI for threat detection. And the data flowing through your network is the fuel that powers intelligent systems.

What Is the AI Cisco Certification Roadmap?

Understanding where AI topics appear across the Cisco certification hierarchy is critical for focused exam preparation. AI is not an isolated certification track --- it is being integrated across multiple levels and specializations.

CCNA v1.1 (Exam 200-301)

At the associate level, the CCNA v1.1 exam now includes objective 6.4, which requires candidates to explain AI (generative vs predictive) and machine learning in network operations. This is a foundational-level requirement, meaning you need to understand the core concepts, differences between AI approaches, and how they apply specifically to network operations scenarios.

This is significant because it signals that AI literacy is no longer optional even for entry-level networking professionals. If you are pursuing your CCNA, you must be able to articulate what predictive AI does versus what generative AI creates, and provide examples relevant to networking.

CCNP Data Center --- Designing Cisco DC Infrastructure (Exam 300-610)

At the professional level, the Designing Cisco DC Infrastructure for Traditional and AI Workloads v1.2 exam includes objective 1.1, which requires candidates to describe AI/ML concepts including application and uses, deep learning and machine learning concepts (inference vs training), and related topics.

This goes deeper than CCNA-level awareness. At the CCNP level, you are expected to understand the difference between training and inference workloads, which has direct implications for how you design data center network fabrics. Training workloads have very different network requirements than inference workloads, and a CCNP-certified professional needs to understand why.

CCDE: AI Infrastructure

At the expert design level, the CCDE now includes a dedicated AI Infrastructure domain covering:

AI and Machine Learning (ML, Deep Learning, LLM, GenAI)
Service placement
Data sovereignty
Regulations and governance
Sustainability

The CCDE-level expectations are the most comprehensive. You are expected to design infrastructure that supports the full AI lifecycle while accounting for regulatory requirements, data sovereignty concerns, and sustainability considerations. This is the level where understanding AI moves from conceptual knowledge to architectural design competency.

Pro Tip: When preparing for any AI Cisco certification exam, map each AI concept back to a networking use case. Examiners are not testing your ability to build neural networks --- they are testing whether you understand how AI changes network design, operations, and security decisions.

How Does the AI Journey Map to Networking Concepts?

One of the most valuable frameworks for understanding AI in the context of networking is the evolutionary progression from basic artificial intelligence through machine learning, predictive AI, generative AI, and finally agentic AI. Each stage represents a significant leap in capability and has distinct implications for network infrastructure.

Machine Learning: The Foundation

Machine Learning (ML) allows computers to automatically generate the rules that will allow them to solve problems, rather than having those rules manually coded by humans. The process works as follows:

Training: ML algorithms ingest a huge amount of data related to the problem, along with the correct answers or descriptions
Model creation: After this lengthy process, the machine acquires knowledge about the topic, resulting in a "trained model"
Inference: The trained model is then used during the inference process to make predictions or decisions on new data

This distinction between training and inference is critical for network engineers because these two phases have vastly different infrastructure requirements. Training demands massive computational resources and high-bandwidth, lossless network fabrics. Inference can often run on lighter infrastructure but requires low latency for real-time applications.

Predictive AI: Forecasting the Future

Predictive AI uses ML models trained on historical data to make predictions about the future. In networking contexts, this includes:

Fault prediction: Identifying network components likely to fail before they actually do
Demand forecasting: Predicting bandwidth utilization patterns to optimize capacity planning
Anomaly detection: Recognizing unusual traffic patterns that might indicate security threats

Predictive AI is already embedded in many network management platforms. When your monitoring system tells you that a particular link is likely to become congested next Tuesday based on historical traffic patterns, that is predictive AI at work.

Generative AI: Beyond Prediction to Creation

Generative AI represents a fundamental shift. Instead of just predicting outcomes, GenAI creates new data similar to what it has learned, using patterns seen in its training data. For network engineers, generative AI manifests in tools that can:

Generate network configurations based on high-level intent descriptions
Create documentation from network state data
Produce troubleshooting recommendations based on symptom analysis
Write automation scripts from natural language descriptions

Agentic AI: The Autonomous Collaborator

The most recent evolution is Agentic AI, which represents AI systems that do not passively respond to commands but instead jump into action to complete tasks end-to-end. Powered by large language models (LLMs), agentic AI systems understand, act, and learn as trusted collaborators.

Key characteristics of agentic AI include:

Autonomous learning, planning, and action: AI agents can independently determine the steps needed to accomplish a goal
Multi-agent collaboration: Agents can collaborate with other agents or humans to complete complex, multi-step tasks
Multi-modal processing: Agentic systems process text, audio, images, and sensor data
Adaptive execution: Combining generative AI's creativity with autonomous decision-making and planning

The agentic AI workflow follows a cycle: Plan, Research, Design a Solution, Generate, Critique, Execute, Interact --- with each step potentially involving specialized AI agents working together.

Pro Tip: For certification exams, be prepared to compare and contrast these AI types. A common exam question pattern asks you to identify which type of AI is appropriate for a given scenario. Predicting link failures is predictive AI. Generating a configuration template is generative AI. An autonomous system that detects a failure, diagnoses the root cause, generates a fix, and applies it is agentic AI.

The Historical AI Timeline

Understanding the historical progression helps contextualize where we are today:

Year	Milestone
1940s	Mathematical, neurophysiology, and computing research foundations
1950s	Turing Test proposed
1956	John McCarthy defines AI as "the science and engineering of making intelligent machines"
1965	Moore's Law established
1997	Deep Blue beats Garry Kasparov at chess
2000s	Big Data Era begins
2009	Google's Self-Driving Car project launches
2012	AlexNet breakthrough in image recognition
2014	Introduction of Alexa voice assistant
2017	"Attention Is All You Need" paper introduces the Transformer architecture
2022	Consumer-facing Large Language Models (LLMs) emerge
2025+	AgenticOps and AI Canvas era begins

The Transformer architecture introduced in 2017 is particularly important to understand because it is the foundation upon which modern LLMs are built. When your certification exam references LLMs, foundational models, or domain-specific models, they all trace back to this architectural innovation.

What AI Infrastructure Must Network Engineers Understand?

For network engineers pursuing AI Cisco certification at any level, understanding AI infrastructure requirements is increasingly non-negotiable. AI workloads place demands on network infrastructure that differ dramatically from traditional enterprise applications.

AI System Architecture

A system for AI involves several layers that work together:

Data Engineering: Preparing and managing the data that feeds AI models
Data Preparation: Cleaning, formatting, and organizing data for training
Model Training/Inference: The actual AI computation
Prompt and Response: The user-facing interaction layer (for GenAI systems)

Each layer has distinct network requirements, and a well-designed AI infrastructure must optimize for all of them simultaneously.

The AI Network Fabric

The infrastructure requirements for AI workloads are substantial and specific:

Inter-GPU Backend Network: This requires high throughput connections at 100G, 400G, or 800G speeds with a lossless fabric specifically optimized for training workloads
Dense GPU Nodes and Clusters: Training requires dense computational resources organized into clusters that communicate frequently and at high bandwidth
Differentiated Compute Density: Training and inference can differ significantly in compute density and utilization patterns, meaning the network must support different traffic profiles
Power and Cooling: AI infrastructure can require extensive power and cooling capacity, which impacts data center design decisions
Monitoring and Observability: A comprehensive monitoring and observability layer must sit on top of the entire infrastructure

The Agentic AI Distribution Challenge

With the emergence of agentic AI, infrastructure design faces a new challenge. AI workloads no longer run only in one centralized cluster as they do during training. With agentic AI, workflows are distributed and continuous. This means:

Network fabrics must support east-west traffic between distributed AI agents
Latency requirements become more complex as agents collaborate across locations
Security policies must account for autonomous agent-to-agent communication
Monitoring must track distributed workflows that span multiple infrastructure components

Pro Tip: When studying AI infrastructure for certification exams, focus on understanding the difference between training and inference network requirements. Training needs high-bandwidth, lossless fabrics because GPUs must synchronize gradients frequently. Inference needs low-latency connectivity because end users expect real-time responses. This distinction appears across CCNP and CCDE exam objectives.

How Are AI Skills Reshaping Network Engineering Job Roles?

The impact of AI on network engineering careers extends far beyond certification exams. Research from the AI Workforce Consortium reveals several critical trends that every networking professional should understand.

The Pervasiveness of AI in Tech Jobs

The data is striking: 78% of the job roles analyzed include AI skills, highlighting a fundamental shift in role requirements across the G7 nations. This is not about a few specialized AI engineering positions --- it is about AI becoming a baseline expectation across the entire technology workforce.

AI Roles Dominating Job Market Growth

Seven of the ten fastest-growing ICT roles are AI-related, including:

AI/ML Engineer
AI Risk and Governance Specialist
NLP (Natural Language Processing) Engineer

This growth means that even if you do not transition into a dedicated AI role, the network engineering positions you compete for will increasingly require AI competency.

The Surge in Specialized AI Skills

The demand for specialized AI skills is growing at remarkable rates:

Skill Area	Growth Rate
AI Security	+298%
Foundation Model Adaptation	+267%
Responsible AI	+256%
Multi-Agent Systems	+245%
AI Governance	+150%
AI Ethics	+125%

The AI landscape is quickly shifting from chatbots to agents, and this shift is driving demand for increasingly specialized competencies. The +298% growth in AI security skills is particularly relevant for networking professionals, as it sits squarely at the intersection of traditional network security and emerging AI capabilities.

AI Ethics and Governance: The Human Side

Demand for expertise in AI governance and AI ethics reflects the growing need for professionals who understand the intersection of technology, law, and ethics. This is not just a compliance checkbox --- it represents a critical technical skills deficit that organizations are urgently trying to fill.

The skills deficit has reached critical levels in areas such as:

Generative AI
Large Language Models (LLMs)
Prompt Engineering
AI Ethics
AI Security

Simultaneously, human skills like communication, collaboration, and leadership are increasingly prioritized for responsible technology adoption. This means that the ideal networking professional of the future combines deep technical AI knowledge with strong interpersonal skills.

What Essential AI Skills Do Network Engineers Need?

Even if your primary expertise is in routing, switching, or security, three foundational AI skill areas are now considered essential for network engineers.

AI Literacy

AI literacy for network engineers means:

Critically selecting and using AI tools appropriate for the task at hand
Responsible use of AI tools in professional contexts
Understanding the ethical aspects of AI, including bias, privacy, and accountability

This is not about becoming an AI researcher. It is about being able to evaluate when an AI-powered network management tool is giving you reliable recommendations versus when it might be leading you astray.

Data Fundamentals

Data competency includes:

Data science principles and techniques relevant to network operations
Data classification methodologies
Basic analytics skills for interpreting network telemetry and AI-generated insights

Network engineers already work with data constantly --- flow records, SNMP counters, syslog messages, streaming telemetry. The shift is toward understanding this data not just as operational information but as potential training data for ML models that can improve network operations.

Prompt Engineering

As AI assistants become standard tools in network operations, prompt engineering becomes a practical daily skill:

Understanding how to interact with AI systems using effective prompts
Mastering prompting techniques that produce accurate and useful outputs
Recognizing the potential and limitations of prompt engineering

Pro Tip: Start practicing prompt engineering now with real networking scenarios. Try asking AI assistants to help you troubleshoot a BGP peering issue, design a VXLAN fabric, or write an Ansible playbook for switch provisioning. The more you practice crafting precise, context-rich prompts, the more effective you will be when these skills are tested on certification exams and evaluated in job interviews.

How Job Roles Are Shifting for Network Engineers

The transformation in job expectations is happening on multiple fronts. Understanding what is becoming less important and what is becoming more important helps you prioritize your professional development.

What Network Engineers Should Do Less Of

The traditional tasks that are becoming automated or AI-assisted include:

Manual CLI configurations: AI-driven intent-based networking is reducing the need for line-by-line configuration
Basic troubleshooting: AI-powered diagnostics can handle common issues faster and more consistently
Reactive troubleshooting: Predictive AI shifts the model from "fix what broke" to "prevent the break"
Routine management tasks: Automation handles repetitive operational work
Basic programming: AI code generation tools can handle straightforward scripting tasks

What Network Engineers Should Do More Of

The skills that are growing in importance include:

Developing AI literacy and prompt engineering skills to effectively interact with AI assistants
Understanding AI technologies and how to manage and leverage AI solutions in network operations
Building data analytics skills to interpret network telemetry and AI-generated insights
Focusing on complex problem-solving, strategic planning, and innovation --- the tasks that AI cannot yet handle independently

What IT Hiring Managers Are Looking For

The hiring landscape is shifting accordingly. Managers increasingly seek:

Candidates with a basic understanding of AI and machine learning concepts
Individuals with AI literacy and prompt engineering skills
Candidates with strong problem-solving and analytical skills to interpret AI-generated insights
Candidates with the ability to manage and properly leverage AI solutions

Conversely, managers are placing less emphasis on:

Candidates with expertise primarily in manual tasks that can now be automated
Professionals with network-only interest or intelligence who lack broader technology awareness
Candidates with only traditional network management skills

Research confirms that cybersecurity and AI certifications are considered the most relevant in today's market, and that cybersecurity and AI skills are the most relevant today. This makes pursuing an AI Cisco certification not just academically interesting but strategically important for career advancement.

Understanding the Cisco AI Strategy for Certification Success

To excel on AI-focused certification questions, you need to understand the two-pronged approach to AI in networking that forms the conceptual framework behind many exam topics.

AI in the Network

AI in the network refers to AI applications and workloads that run within networking products and solutions. This includes:

AI-powered analytics engines embedded in network management platforms
Machine learning algorithms that optimize routing decisions
Natural language interfaces for network configuration and troubleshooting
Predictive maintenance capabilities built into network operating systems

When your exam asks about how AI improves network operations, it is asking about AI in the network.

AI on the Network

AI on the network refers to the network infrastructure and connectivity required to support AI workloads. This includes:

High-performance data center fabrics designed for GPU-to-GPU communication
Lossless Ethernet implementations for training cluster interconnects
Network architectures optimized for the unique traffic patterns of AI workloads
Quality of service policies tailored to AI training and inference traffic

When your exam asks about designing infrastructure for AI workloads, it is asking about AI on the network.

This distinction is fundamental to how AI topics are organized across Cisco certifications. CCNA primarily tests your understanding of AI in the network. CCNP Data Center tests both concepts. CCDE tests your ability to design AI on the network at an architectural level.

Pro Tip: The statement "AI won't replace humans --- but humans with AI will replace humans without AI" captures the philosophy behind the integration of AI into Cisco certifications. The exams are not trying to turn network engineers into data scientists. They are ensuring that certified professionals can work effectively alongside AI systems and design infrastructure that supports AI workloads.

How AI Cisco Certification Topics Connect to Real-World Infrastructure

Understanding the connection between certification exam topics and real-world infrastructure design is essential for both passing exams and applying knowledge on the job.

From Centralized Training to Distributed Inference

Traditional AI workloads followed a simple pattern: train a model in a centralized GPU cluster, then deploy it for inference. The network requirements were relatively straightforward --- build a high-bandwidth, lossless fabric for the training cluster and provide low-latency connectivity for inference endpoints.

With agentic AI, this model is changing. Workflows are distributed and continuous. AI agents running in different locations need to communicate, collaborate, and share results in real time. This creates new challenges for:

Network segmentation: How do you apply security policies to autonomous AI agents?
Quality of service: How do you prioritize agent-to-agent traffic alongside traditional application traffic?
Monitoring: How do you trace a distributed AI workflow across multiple network segments?
Capacity planning: How do you predict the network impact of autonomous agents that can spawn new tasks dynamically?

The Convergence of Infrastructure, Security, and AI

The modern networking professional must understand the convergence of three domains:

Infrastructure to power AI: Building the network fabrics, compute clusters, and storage systems that AI workloads require
Security for AI and AI for security: Protecting AI systems from attack while leveraging AI to improve security posture
Data to drive insights and context: Managing the flow of data that feeds AI models while ensuring data sovereignty and regulatory compliance

This convergence is reflected in the breadth of AI topics appearing across different Cisco certification tracks. It is no longer sufficient to be an expert in just one domain --- the boundaries between infrastructure, security, and data are blurring, and AI sits at the center of that convergence.

Software and Services in the AI Era

Two additional dimensions complete the picture:

Software to unlock productivity: AI-powered software tools that make network engineers more productive, from automated configuration generation to intelligent troubleshooting assistants
Services to accelerate the value of AI: Professional services methodologies for planning, deploying, and optimizing AI-ready infrastructure

And tying it all together: certifications to empower AI innovation and infrastructure. This is precisely why AI topics are being integrated across the entire Cisco certification portfolio rather than being confined to a single specialty track.

Preparing for AI Topics on Cisco Certification Exams

With a clear understanding of what AI topics appear on exams and why they matter, here is a practical study strategy for AI Cisco certification preparation.

For CCNA Candidates

Focus on understanding the conceptual differences between:

Predictive AI vs. Generative AI: Know what each does, how they differ, and provide networking-specific examples of each
Machine Learning fundamentals: Understand the training-model-inference pipeline at a high level
AI in network operations: Be able to explain how AI improves network monitoring, troubleshooting, and management

You do not need deep mathematical understanding of neural networks for the CCNA. Focus on being able to explain these concepts clearly and map them to practical networking scenarios.

For CCNP Data Center Candidates

Go deeper into:

Training vs. inference workloads: Understand the different compute, storage, and network requirements for each
Deep learning vs. machine learning: Know how deep learning extends ML capabilities and why it matters for network applications
AI/ML applications in data center design: Be prepared to explain how AI workload requirements influence data center fabric design decisions

For CCDE Candidates

Master the architectural implications:

AI infrastructure design: Understand how to design networks that support AI workloads at scale, including GPU cluster interconnects and distributed inference architectures
Data sovereignty and regulatory compliance: Know how data residency requirements affect AI infrastructure placement decisions
Sustainability: Understand the power and cooling implications of AI infrastructure and how they affect design choices
Service placement: Be able to design architectures that optimize the placement of AI services across distributed infrastructure

Building a Study Plan

Start with the fundamentals: Make sure you can explain ML, predictive AI, generative AI, and agentic AI in your own words
Map concepts to networking: For every AI concept, identify at least one networking use case
Understand infrastructure requirements: Know the difference between training and inference network needs
Follow industry trends: Track the growth in AI skills demand and understand which skills are most valued
Practice with AI tools: Use AI assistants in your daily networking work to build practical prompt engineering skills

Frequently Asked Questions

Do I Need Programming Skills to Pass AI Topics on Cisco Certification Exams?

Not at the CCNA level. The AI topics on the CCNA v1.1 exam focus on conceptual understanding --- being able to explain the difference between generative and predictive AI and how machine learning applies to network operations. At the CCNP and CCDE levels, you need deeper architectural understanding but still not the ability to write ML code. The emphasis is on understanding AI workload requirements and how they influence network design decisions.

Which Cisco Certification Has the Most AI Content?

Currently, the CCDE AI Infrastructure domain has the most comprehensive AI coverage, including ML, deep learning, LLMs, GenAI, service placement, data sovereignty, regulations, governance, and sustainability. However, AI topics are being integrated across multiple certification tracks. The CCNP Data Center track (exam 300-610) has significant AI content focused on designing infrastructure for AI workloads, and even the CCNA now includes AI fundamentals as an exam objective.

What Is the Difference Between AI in the Network and AI on the Network?

AI in the network refers to AI-powered features embedded within networking products and solutions --- think intelligent analytics, automated troubleshooting, and ML-driven optimization within your network management platform. AI on the network refers to the network infrastructure required to support AI workloads --- the high-bandwidth, lossless fabrics connecting GPU clusters for training, and the low-latency paths needed for inference. Both concepts appear across Cisco certification exams at different levels of depth.

How Important Are AI Skills Compared to Traditional Networking Skills?

Both are essential, but the balance is shifting. Research shows that 78% of ICT job roles now include AI skills, and seven of the ten fastest-growing ICT roles are AI-related. However, this does not mean traditional networking skills are obsolete. The most valuable professionals combine deep networking expertise with AI literacy. As hiring trends show, managers are looking less for candidates with only traditional network management skills and more for those who can also manage and leverage AI solutions.

What Is Agentic AI and Why Does It Matter for Network Engineers?

Agentic AI represents AI systems that autonomously plan, execute, and adapt to complete complex tasks end-to-end, rather than passively responding to individual commands. For network engineers, agentic AI matters because it is changing infrastructure requirements. Unlike traditional AI workloads that run in centralized clusters, agentic AI workflows are distributed and continuous, meaning network architectures must support agent-to-agent communication across multiple locations with varying latency and bandwidth requirements.

Should I Wait for AI-Specific Cisco Certifications or Start Studying Now?

Start now. AI topics are already present on current exams --- CCNA v1.1 (objective 6.4), CCNP Data Center (exam 300-610, objective 1.1), and CCDE (AI Infrastructure domain). The demand for AI skills is growing rapidly, with AI security skills demand up 298% and foundation model adaptation up 267%. Building your AI knowledge today gives you an advantage on current exams and prepares you for the expanding AI content that future exam revisions will undoubtedly include.

Conclusion

The integration of AI into Cisco certifications reflects a broader transformation in the networking industry. AI is not a separate discipline that network engineers can choose to ignore --- it is becoming a fundamental component of how networks are designed, deployed, operated, and secured.

The key takeaways from this deep dive into AI Cisco certification topics are clear:

AI topics are already on current exams across CCNA, CCNP, and CCDE tracks, with increasing depth at each level
The AI journey from ML to agentic AI represents a progression that every networking professional must understand, with each stage having distinct infrastructure implications
Job roles are actively shifting --- 78% of ICT roles now include AI skills, and hiring managers are prioritizing candidates with AI literacy alongside traditional networking expertise
Three foundational skills --- AI literacy, data fundamentals, and prompt engineering --- are now considered essential even for network engineers
AI infrastructure demands specialized network fabrics with high throughput (100G, 400G, 800G) and lossless characteristics that differ significantly from traditional enterprise networking

The professionals who will thrive in this new landscape are those who embrace AI as a force multiplier for their existing networking expertise. As the research makes clear: AI will not replace humans, but humans with AI will replace humans without AI.

Start building your AI skills today. Explore the certification-aligned courses available at NHPREP to accelerate your preparation for AI topics across the Cisco certification portfolio. Whether you are just beginning your CCNA journey or designing expert-level architectures at the CCDE level, investing in AI knowledge now will pay dividends throughout your career.