Cisco AI Technical Practitioner (810-110 AITECH) v1.0 Cert Prep by Pearson

Course details

Prepare to become a certified Cisco AI Technical Practitioner in this comprehensive exam prep course. Explore generative AI models, including cloud-hosted versus locally hosted solutions, token management, and model hub selection. Learn effective prompt engineering, along with defenses against threats such as prompt injection and AI hallucinations. Review the principles of ethical AI deployment, ensuring fairness, accountability, and data privacy. Discover automated data preparation techniques that streamline research and analysis, and how to apply AI to content drafting and ideation. Along the way, learn how to enhance your software development workflows with AI-assisted code generation, optimization, and integration across the lifecycle. This course is an ideal fit for IT professionals, solutions architects, and technical leaders adopting AI in their organizations.

Learning objectives
Explain the fundamentals of generative AI models, including hosting options, token management, and model hub selection.
Apply prompt engineering techniques, including defenses against prompt injection and AI hallucinations.
Evaluate the principles of ethical AI deployment, including fairness, accountability, and data privacy.
Integrate AI into data preparation and software development workflows.
Describe how agentic AI systems pursue multistep goals under human oversight and governance.

Concepts

Introduction

Cisco AI Technical Practitioner 810-110 AITECH - Introduction

Generative AI Models

Learning objectives
Introduction to generative AI models
Model hosting options
Context windows and token management
Model selection in AI hubs
Using open weight models and introducing Hugging Face
Retrieval-augmented generation (RAG), embeddings, and vector databases
Understanding agentic RAG
Vector databases and embedding models

Prompt Engineering

Learning objectives
Introduction to prompt engineering
Prompt engineering principles and patterns
Prompting techniques
Prompt injection attack types
Defensive prompting and error mitigation
Exploring the OWASP generative AI security resources
Introducing the Coalition for Secure AI (CoSAI)
Introducing the MITRE ATLAS

Ethics and Security

Learning objectives
Introduction to AI ethics and security
Responsible AI principles
Corporate data privacy and security approaches
AI security threats and risks
AI governance considerations

Data Research and Analysis

Learning objectives
Introduction to data research and analysis
AI's role in exploratory data analysis (EDA)
Automated data preparation
Ethical and privacy considerations
AI-assisted research, ideation, and content drafting

Development and Workflow Automation

Learning objectives
Introduction to AI for code and workflow optimization
AI's role across the software development lifecycle
Code generation and rapid prototyping
AI workflow design and monitoring
Token usage and context window management
Code quality improvement with AI
Deep diving into Cursor
Using Windsurf and Cascade
Understanding agent skills, rules, memories, and workflow files
Exploring OpenAI's Codex
Using Claude Code
Using AI for secure code review
Using additional tools like Antigravity, OpenCode, and Warp

Agentic AI

Learning objectives
Introduction to agentic AI
Agentic AI vs. generative AI
Agentic AI design principles
Model Context Protocol (MCP)
Introducing WebMCP
Human-in-the-loop (HITL) and human-on-the-loop (HOTL) strategies
Data transformation in AI agents
Agent skills and harnesses

Conclusion

Next steps and exam tips