Cisco AI Technical Practitioner (810-110 AITECH)

Course Overview

The Cisco AI Technical Practitioner (810-110 AITECH) training and certification program is designed for IT professionals, network engineers, automation specialists, solutions architects, developers, and technical teams who want to build practical expertise in applying Artificial Intelligence (AI) to modern technical workflows.

This certification validates your ability to understand, evaluate, and implement AI-driven solutions for automation, coding, analytics, workflow optimization, and business operations. The course focuses on generative AI models, prompt engineering, AI security, data analysis, workflow automation, and agentic AI systems.

Offered by Linux Training Center, Coimbatore, this course aligns with the official Cisco 810-110 AITECH objectives and provides hands-on practical training in AI-powered productivity tools, automation workflows, prompt design, AI-assisted coding, and intelligent decision-making systems.

Who Should Enroll?

  • IT professionals exploring AI-driven workflows
  • Network engineers adopting AI-powered automation
  • Developers and software engineers
  • DevOps and automation specialists
  • Solutions architects
  • Technical managers and team leads
  • Professionals pursuing Cisco AI certifications

Why This Course Stands Out

  • Complete coverage of Cisco AITECH objectives
  • Hands-on labs with real-world AI tools and workflows
  • Practical training in AI-powered automation and productivity
  • Strong focus on prompt engineering and AI security
  • Real-world enterprise AI use cases
  • Certification-focused practical exercises
  • Industry-aligned curriculum for AI-driven technical roles

Career Roles You Can Pursue

  • AI Engineer
  • AI Automation Specialist
  • Network Automation Engineer
  • Solutions Architect
  • DevOps Engineer
  • AI Workflow Specialist
  • Technical Consultant
  • Automation Engineer

Why Choose Linux Training Center, Coimbatore?

  • Expert instructors with AI and Cisco technology expertise
  • Hands-on practical labs with real AI tools
  • Flexible weekday and weekend batch schedules
  • Comprehensive study materials and lab access
  • Real-world AI workflow exercises
  • Career guidance and placement assistance
  • Post-training mentorship until certification completion

Become an AI-Powered Technical Professional

Advance your career with Cisco AI Technical Practitioner (810-110 AITECH) certification training. Gain practical expertise in generative AI, prompt engineering, AI security, automation, and intelligent workflows to excel in modern AI-driven IT and enterprise environments.

Modules

Generative AI Models
  • Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation)
  • Compare model hosting options (cloud-hosted vs locally hosted) and their trade-offs (cost, latency, privacy, scalability)
  • Explain role of context windows, token limits and response management
  • Understand model selection in AI model hubs and repositories for appropriate use-cases (e.g., reasoning, multimodality)
  • Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases
    • Prompt Engineering
  • Understand prompt engineering principles and patterns (roles, instructions, constraints)
  • Explain prompting techniques (iterative/sequential, chained, few-shot) and structures for text, image and audio generation
  • Describe prompt injection attack types
  • Explain defensive prompting and mitigation strategies for AI-generated errors (e.g., hallucinations)
    • Ethics and Security
  • Explain responsible AI principles (fairness, transparency, accountability, bias mitigation, safety)
  • Describe approaches to protect corporate data privacy and security in AI systems
  • Explain AI-specific security threats and risks, including misinformation
  • Explain AI governance considerations (policy, risk management, compliance)
    • Data Research and Analysis
  • Explain AI’s role in exploratory data analysis (EDA)
  • Describe automated data preparation tasks (quality checks, formatting, transformation, cleaning)
  • Explain the ethical and privacy considerations in AI-assisted data analysis, including controls to prevent data exposure
  • Describe techniques for AI-assisted research, ideation, and content drafting
    • Development and Workflow Automation
  • Describe AI's role across the software development lifecycle (requirements, prototyping, implementation, testing, deployment)
  • Describe the AI capabilities for code generation and rapid prototyping
  • Explain AI workflow design and monitoring principles
  • Describe how token usage and context-window management affect prototyping cost, latency, and output quality
  • Explain how AI improves code quality (debugging assistance, error handling, documentation)
    • Agentic AI
  • Differentiate Agentic AI from Generative AI use cases
  • Explain AI agent design principles, autonomous capabilities, and orchestration
  • Describe Model Context Protocol (MCP) framework primitives in context of agentic AI
  • Explain human-in-the-loop (HITL) strategies
  • Describe data transformation and mapping within AI Agents

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