You can see it againUnder pressure people bring back shapes and positions long after the wording has gone.
Picture superiority · Shepard 1967, Standing 1973
CISCOCisco AI Technical Practitioner
The Cisco AI Technical Practitioner certification validates your ability to modernize code, automate workflows, and design AI-powered solutions through hands-on labs and real-world scenarios. Built for IT professionals who want to embed generative AI into daily operations, this credential proves you can apply prompt engineering, AI ethics, and agentic AI to drive adoption and innovation within your organization.
Exam formatPass/fail
Duration60 minutes
DeliveryPearson VUE
Free questions607
Content last reviewed 30 July 2026 · Up to date
The certification
What Cisco AI Technical Practitioner proves, and what it asks of you
What this certification covers, who it is written for, and what the exam itself looks like on the day.
6domains
27objectives
142concepts
US $150exam fee
What it is
What this certification is
What it validates, who it is written for, and the experience it assumes.
About this certification
The Cisco AI Technical Practitioner (AITECH) certification equips IT professionals with the practical skills to integrate generative AI into their daily workflows. By passing the 810-110 AITECH exam, you demonstrate proficiency in generative AI models, prompt engineering, AI ethics and security, data research and analysis, AI for code and workflow optimization, and agentic AI. The certification emphasizes hands-on application through labs and real-world scenarios, ensuring you can immediately apply what you learn.
This certification is part of Cisco's AI certification portfolio, designed to help you stay at the forefront of AI innovation. Whether you are a developer, engineer, or IT operations professional, earning the AITECH credential signals to employers that you can lead AI adoption, optimize operations, and drive measurable business outcomes. With AI transforming every industry, this certification positions you to champion AI initiatives and future-proof your career.
Who it’s for
This certification is designed for IT professionals who want to leverage AI to modernize code, automate workflows, and design AI-powered solutions. It is ideal for developers, engineers, and technical practitioners who are responsible for integrating AI into their organization's operations and driving AI adoption. Candidates should have a foundational understanding of AI concepts and be comfortable working with code and automation tools. The certification is also valuable for those looking to stand out in the AI-driven job market and demonstrate their ability to apply AI in real-world scenarios.
Recommended experience
Cisco recommends studying with Cisco U. Learning Paths and hands-on labs, but does not mandate specific experience. A background in IT and familiarity with AI concepts is beneficial. Familiarity with generative AI models and prompt engineering; Basic understanding of AI ethics and security considerations; Experience with coding and workflow automation; Interest in agentic AI and its applications
The syllabus
What you’ll learn
Every domain and objective Cisco measures, with the weight they carry on the exam.
The official Cisco exam outline · checked 30 July 2026 · See the source
Generative AI Models
- 1.1 Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation)
- 1.2 Compare model hosting options (cloud-hosted vs locally hosted) and their trade-offs (cost, latency, privacy, scalability)
- 1.3 Explain role of context windows, token limits and response management
- 1.4 Understand model selection in AI model hubs and repositories for appropriate use‑cases (e.g., reasoning, multimodality)
- 1.5 Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases
Prompt Engineering
- 2.1 Understand prompt engineering principles and patterns (roles, instructions, constraints)
- 2.2 Explain prompting techniques (iterative/sequential, chained, few‑shot) and structures for text, image and audio generation
- 2.3 Describe prompt injection attack types
- 2.4 Explain defensive prompting and mitigation strategies for AI-generated errors (e.g., hallucinations)
Ethics and Security
- 3.1 Explain responsible AI principles (fairness, transparency, accountability, bias mitigation, safety)
- 3.2 Describe approaches to protect corporate data privacy and security in AI systems
- 3.3 Explain AI-specific security threats and risks, including misinformation
- 3.4 Explain AI governance considerations (policy, risk management, compliance)
Data Research and Analysis
- 4.1 Explain AI’s role in exploratory data analysis (EDA)
- 4.2 Describe automated data preparation tasks (quality checks, formatting, transformation, cleaning)
- 4.3 Explain the ethical and privacy considerations in AI-assisted data analysis, including controls to prevent data exposure
- 4.4 Describe techniques for AI-assisted research, ideation, and content drafting
Development and Workflow Automation
- 5.1 Describe AI's role across the software development lifecycle (requirements, prototyping, implementation, testing, deployment)
- 5.2 Describe the AI capabilities for code generation and rapid prototyping
- 5.3 Explain AI workflow design and monitoring principles
- 5.4 Describe how token usage and context‑window management affect prototyping cost, latency, and output quality
- 5.5 Explain how AI improves code quality (debugging assistance, error handling, documentation)
Agentic AI
- 6.1 Differentiate Agentic AI from Generative AI use cases
- 6.2 Explain AI agent design principles, autonomous capabilities, and orchestration
- 6.3 Describe Model Context Protocol (MCP) framework primitives in context of agentic AI
- 6.4 Explain human-in-the-loop (HITL) strategies
- 6.5 Describe data transformation and mapping within AI Agents
On the day
The exam itself
Everything Cisco publishes about sitting it, and nothing we inferred.
Prerequisites
No mandatory prerequisites — this certification has no required predecessor exam or credential.
CertificationCisco AI Technical Practitioner
Exam formatPass/fail
Duration60 minutes
DeliveryPearson VUE
LanguagesEnglish
PricingUS $150
Certification levelPractitioner
After you pass
Where this credential goes next
The path Cisco lays out, how the credential is kept, and where to book.
Step-by-step path to Cisco AI Technical Practitioner
Credential earnedCisco AI Technical Practitioner Practitioner level certification Renewal and maintenance
Cisco certifications are valid for three years. You can recertify by passing another certification exam or earning Continuing Education (CE) credits. Stay current with the latest technologies and maintain your certification.
Learn more about renewal requirements Lifecycle status
This certification is currently active and available. Cisco maintains this certification to validate current skills and industry relevance.
Exam status: ActiveMaintained by Cisco
Exam registration
Register for the exam through Pearson VUE, Cisco’s authorized testing partner.
Schedule your examVisit the official Cisco certification page for exam policies and requirements.
View the official pageYour coach
And when you are serious, your coach Pip takes over
Your coach in the app reads what you have answered with the book closed and tells you one thing to do tonight. It will not count an answer you gave with the page open, and it will tell you when you are not ready.
See how the coach worksBefore you book
Questions people ask
How does the AITECH exam relate to the Cisco AI Business Practitioner (AIBIZ) certification?
AITECH is a technical certification focused on hands-on AI skills like prompt engineering and workflow automation, while AIBIZ is a business-focused certification for championing AI solutions. They are separate certifications; AITECH is not a prerequisite for AIBIZ.
Is there a hands-on lab component in the AITECH exam?
The exam includes labs and real-world scenarios as part of the learning path, but the exam itself is a written exam. Hands-on practice is recommended through Cisco U. Learning Paths.
What is the retake policy if I fail the AITECH exam?
For most Cisco exams, you must wait 5 calendar days after your first attempt before retaking the same exam. For CCIE/CCDE written exams, the wait is 15 days. Check the Cisco Certification Tracking System for specific details.
Can I use Cisco Learning Credits to pay for the AITECH exam?
Yes, the exam price is $US150, or you can use Cisco Learning Credits. Learning Credits can be redeemed for exam vouchers through Cisco.
What job roles does the Cisco AI Technical Practitioner certification map to?
This certification is designed for IT professionals who want to integrate AI into their workflows, such as developers, engineers, and technical practitioners responsible for AI adoption and automation.
Can I recertify by passing a different Cisco exam?
Yes, you can recertify by passing another certification exam or by earning Continuing Education (CE) credits. Passing a higher-level exam may also renew your certification.
How soon will I receive my exam results?
Results are available online within 48 hours of completing the exam. You can view them by logging into the Cisco Certification Tracking System.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 607 questions, free, no account needed.
The curriculum
The whole exam, mapped and written
Every domain, every objective, and every concept Cisco measures — each one written out.
6domains
27objectives
142concepts
131free pages
The domains, and what sits inside them
Every objective below is a page you can open and practise now, without an account.
The official Cisco exam outline · checked 30 July 2026 · See the source
1Generative AI Models93 questions
1.1 Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation)6 concepts · 4 pagesOpen →1.2 Compare model hosting options (cloud-hosted vs locally hosted) and their trade-offs (cost, latency, privacy, scalability)5 concepts · 2 pagesOpen →1.3 Explain role of context windows, token limits and response management3 concepts · 5 pagesOpen →1.4 Understand model selection in AI model hubs and repositories for appropriate use‑cases (e.g., reasoning, multimodality)6 concepts · 4 pagesOpen →1.5 Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases4 concepts · 5 pagesOpen →
2Prompt Engineering104 questions
2.1 Understand prompt engineering principles and patterns (roles, instructions, constraints)4 concepts · 4 pagesOpen →2.2 Explain prompting techniques (iterative/sequential, chained, few‑shot) and structures for text, image and audio generation6 concepts · 5 pagesOpen →2.3 Describe prompt injection attack types10 concepts · 6 pagesOpen →2.4 Explain defensive prompting and mitigation strategies for AI-generated errors (e.g., hallucinations)9 concepts · 7 pagesOpen →
3Ethics and Security94 questions
3.1 Explain responsible AI principles (fairness, transparency, accountability, bias mitigation, safety)6 concepts · 5 pagesOpen →3.2 Describe approaches to protect corporate data privacy and security in AI systems4 concepts · 4 pagesOpen →3.3 Explain AI-specific security threats and risks, including misinformation4 concepts · 5 pagesOpen →3.4 Explain AI governance considerations (policy, risk management, compliance)6 concepts · 6 pagesOpen →
4Data Research and Analysis87 questions
4.1 Explain AI’s role in exploratory data analysis (EDA)8 concepts · 7 pagesOpen →4.2 Describe automated data preparation tasks (quality checks, formatting, transformation, cleaning)4 concepts · 4 pagesOpen →4.3 Explain the ethical and privacy considerations in AI-assisted data analysis, including controls to prevent data exposure4 concepts · 5 pagesOpen →4.4 Describe techniques for AI-assisted research, ideation, and content drafting3 concepts · 3 pagesOpen →
5Development and Workflow Automation126 questions
5.1 Describe AI's role across the software development lifecycle (requirements, prototyping, implementation, testing, deployment)6 concepts · 5 pagesOpen →5.2 Describe the AI capabilities for code generation and rapid prototyping4 concepts · 5 pagesOpen →5.3 Explain AI workflow design and monitoring principles5 concepts · 6 pagesOpen →5.4 Describe how token usage and context‑window management affect prototyping cost, latency, and output quality6 concepts · 6 pagesOpen →5.5 Explain how AI improves code quality (debugging assistance, error handling, documentation)3 concepts · 5 pagesOpen →
6Agentic AI103 questions
6.1 Differentiate Agentic AI from Generative AI use cases6 concepts · 4 pagesOpen →6.2 Explain AI agent design principles, autonomous capabilities, and orchestration3 concepts · 4 pagesOpen →6.3 Describe Model Context Protocol (MCP) framework primitives in context of agentic AI6 concepts · 5 pagesOpen →6.4 Explain human-in-the-loop (HITL) strategies5 concepts · 5 pagesOpen →6.5 Describe data transformation and mapping within AI Agents6 concepts · 5 pagesOpen →
Not a table of contents
Every line on this map already opens
In front of every objective the practice pages are already there, free and without an account. This is one objective, opened.
1.1 Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation)4 pagesOpen →
Free in front of it
18 questions on this objective, five to a page. Every range above is a real page, open now, with no account.
Reading the map is not the same as knowing the ground
The curriculum tells you what is on the exam. Proving you know it is a different job — and it is the one the closed-book run does.
Free questions
607 questions. No account, no card.
The whole bank is open. 5 questions to a page, every answer explained, and a discussion thread on each one.
607questions
131pages
27objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Generative AI Models93 questions
2Prompt Engineering104 questions
3Ethics and Security94 questions
4Data Research and Analysis87 questions
5Development and Workflow Automation126 questions
6Agentic AI103 questions
What a practice page gives you
1Five to a page
Short enough to finish, long enough to matter.
2Every answer explained
Not only which one is right — why the others are wrong.
3A discussion on each
Ask, answer, and vote. Every question has its own thread.
A question, and the idea behind it
Every question comes from a concept
These are not trivia. Each one is written against a concept in the book, so when you get one wrong there is somewhere to go and find out why.
The visual book
See it once. Carry it into the exam.
The pages shown here come from our AI-900 book — an example of how each concept is written in plain language and, where the idea needs one, drawn as a full page you can take in at a glance.
142concepts
607free questions
Why a picture, and not another paragraph
Three reasons, and each one is a real finding rather than a slogan.
You can see it againThe whole idea at onceWhere it starts, what happens in the middle, what comes out, and the mistake to avoid.
Multimedia principle · MayerThe look-alikes sit togetherThe pairs the exam tests are drawn side by side, so the difference is seen, not told.
Dual coding · PaivioWhat one page is made of
You are never asked to read a poster here — only to see how one is built. After that, every other page is legible at a glance.
1A numbered strip on top
The idea as a sequence, followed with a finger before a word is read.
2Panels for the parts
What it is, how the machine learns it, when it is the right tool.
3The trap, framed
The distinction the exam tests, given its own box instead of buried in prose.
4One line at the bottom
The sentence to carry into the exam room.
Open book, on purpose
Answer it with the picture beside you
This is the part that teaches. The illustration and the written explanation stay where they are while you work, so a scenario stops being a memory test and becomes something you can simply look at.
It teaches · it does not count
A smartphone uses AI to unlock when the owner looks at the camera. Which AI capability is being used?
AImage classification
BFacial recognition
COptical character recognition
DObject detection
Look at the five steps. Detect finds a face, align straightens it, encode turns it into numbers, search compares it with a stored face, and decide answers one question: is this the same person? Classification would only say “a face”. Detection would only draw a box. Neither decides whose.
Then the page shuts, and it starts counting
The same questions come back with the book closed — that run is the one that counts. After it, your coach picks one thing for tonight, sized to the time you have, and brings pages back before you lose them.
Testing effect · Roediger & Karpicke 2006 · spacing effect · Cepeda et al. 2006
Official resources
Straight from Cisco
Where the exam is defined, scheduled and scored.
3official links
142concepts mapped
The source
Cisco’s own pages
We link to them rather than repeat them, so nothing here goes stale behind them.
Checked against the source
Every objective on the official Cisco outline already has questions here
We build from the official skills outline, not from a summary of it — 27 objectives, 142 concepts written under them, and free questions against every one. When Cisco changes the outline, this page changes with it.
27objectives on the outline→142concepts written→607free questions
An official page tells you what will be asked. It cannot tell you what you will get right.
That is the only question worth answering the night before, and no link answers it. You answer it by taking the questions with the book closed, and seeing what comes back.