Cisco AI Solutions for Cisco Technologies (AITECH) 810‑110 Practice Exams
About the Cisco 810-110 AITECH exam
Exam at a glance
Specialist tier. The qualifying exam for the Cisco Certified Specialist – AI Solutions credential. Approximately 55–65 questions, 90 minutes, Pass/Fail outcome, $300 USD. Valid 3 years.
810-110 is Cisco's accessible on-ramp to AI-focused certification — there are no Cisco-certification prerequisites, and the exam is positioned for IT professionals who want to formalize a working understanding of generative AI, prompt design, retrieval-augmented generation, and how those patterns land on Cisco platforms. Passing 810-110 also earns Continuing Education credits toward CCNP and CCIE recertification.
Domain weighting
- Generative AI Models — 20%
- Prompt Engineering — 15%
- Ethics and Security — 15%
- Data Research and Analysis — 10%
- Development and Workflow Automation — 20%
- Agentic AI — 20%
Where 810-110 fits in the Cisco track
Unlike the 200-, 300-, and 350-series exams which sit inside the CCNA → CCNP → CCIE ladder for a specific technology track (Enterprise, Security, Data Center, Collaboration, Service Provider), 810-110 is a standalone specialist exam. You don't need a CCNA to take it, and it doesn't lead to a Professional or Expert tier — instead it earns a portable specialist badge that signals AI competency on top of whatever Cisco track you already work in.
Prerequisites
None formally required. Cisco recommends 6–12 months of working exposure to generative AI tools (chat assistants, RAG pipelines, AI-assisted code or operations) plus baseline familiarity with IT operations. If you've never touched a Cisco exam before, AITECH is one of the lower-friction entry points to the certification program.
Why take this certification
- Cisco's newest specialist credential. AITECH was introduced as part of Cisco's AI portfolio expansion and is designed to validate practical AI literacy for network, security, and collaboration practitioners — not theoretical ML research skills.
- No Cisco-certification prerequisites. Unlike most of Cisco's catalog, AITECH does not require CCNA or any concentration exam. That makes it a realistic first Cisco credential for IT professionals coming from non-networking AI backgrounds.
- Career-relevant in 2026. Job postings for network and security engineers increasingly list "experience integrating AI/LLM tooling into operations" as a differentiator. A specialist certification on the topic gives that experience a name on your résumé.
- Continuing Education value. Passing 810-110 also earns CE credits toward CCNP / CCIE recertification — so engineers already on the Cisco ladder get double value from a single sitting.
What you'll learn in the 810-110 AITECH exam
AITECH validates that you can reason about generative-AI systems the way a working IT practitioner does — understanding what an LLM is doing under the hood, picking the right prompt or RAG pattern for a workload, and recognizing the safety and governance trade-offs when AI lands inside a Cisco network, collaboration, or security platform. The exam is concept-driven; you will not be writing Python or training models.
AI fundamentals you'll be tested on
- Model anatomy: tokens, embeddings, attention, context windows, parameter counts, the difference between training and inference.
- Model families: when to reach for a small specialized model vs a frontier model, latency vs cost vs quality trade-offs, open- vs closed-weight choices.
- Inference behaviors: temperature, top-p, deterministic vs sampled outputs, why the same prompt can return different answers.
- Multimodal basics: when image / audio / video understanding adds value vs when a text-only model is the right answer.
Prompt engineering patterns
- Zero-shot, few-shot, and chain-of-thought prompting — when each helps and when each is overkill.
- System-prompt vs user-prompt separation, role conditioning, and persona constraints.
- Structured-output patterns (JSON mode, function calling, tool use) for integrating LLMs into workflows that downstream systems must parse.
- Common failure modes: prompt injection, jailbreaks, instruction leaking — and the mitigations.
Retrieval-augmented generation (RAG)
- Why RAG exists: keeping models current without retraining, citing sources, isolating sensitive data from the base model.
- Embedding generation, vector databases, similarity search, hybrid (lexical + vector) retrieval.
- Chunking strategies — size, overlap, semantic vs fixed-window — and their impact on answer quality.
- Evaluating RAG quality: groundedness, citation accuracy, retrieval recall vs precision.
AI on Cisco platforms
- AI-assisted features inside Webex (meeting summaries, real-time translation, intelligent assistant).
- AI use cases in Cisco security tooling: anomaly detection, alert triage, natural-language policy querying.
- How AI workloads change network requirements — east-west traffic patterns, GPU cluster networking, data-pipeline bandwidth.
- Cisco Secure AI Defense and similar guardrail products positioned around enterprise AI deployments.
AI safety and governance
- Hallucination, bias, and data-leakage risks — and the controls used to mitigate them.
- Data residency, privacy, and regulatory considerations (GDPR-style and AI Act-style) when routing prompts to external model providers.
- Prompt-injection and jailbreak defenses at the application layer.
- Responsible-AI principles: transparency, accountability, human-in-the-loop, model evaluation hygiene.
How the practice exams help
Each free question and every premium exam mirrors the scenario style Cisco uses for AITECH — short situational stems, four to six plausible options, with detailed explanations that cover both why the correct answer is correct and why the distractors fail. The point is to build the trade-off intuition the exam tests, not to memorize answer letters.
How to prepare for the Cisco 810-110 AITECH exam
AITECH rewards working familiarity with generative-AI tooling more than book theory. A realistic prep plan blends concept review, hands-on time with at least one chat assistant + one RAG pipeline, and timed practice exams to surface gaps.
- Walk the official exam topics (1–2 weeks). Pull the published 810-110 exam topics list from Cisco Learning Network and skim every bullet. Mark the ones you can already explain to a colleague — that's your "skip" pile. Everything unmarked is your reading list.
- Hands-on AI time (2–3 weeks). Spend real keyboard time with at least one frontier chat assistant and at least one RAG pipeline (LangChain, LlamaIndex, or a vendor-managed equivalent are all fine). Build something tiny — a personal docs Q&A — so prompt design, embedding choice, and chunking become muscle memory rather than vocabulary.
- Cisco-platform reading (1 week). Read the Webex AI Assistant overview, Cisco Secure AI Defense product brief, and any current AI-related Cisco blog posts. You don't need to deploy these products — you need to recognize where they fit in scenario questions.
- Safety, governance, and regulation (1 week). Read a short responsible-AI primer (Microsoft, Google, and NIST all publish accessible ones), plus a one-pager on the EU AI Act risk tiers. Cisco will not ask you to recite regulation text — they will ask you to recognize when a scenario triggers governance concerns.
- Practice exams (1–2 weeks). Take timed practice tests, then read every explanation — including for questions you got right. Aim for consistent 80%+ before scheduling. The detailed answer explanations are where the real learning happens.
Recommended timeline
4–6 weeks for candidates who already work with AI tooling day-to-day. 8–12 weeks if AI is a new topic, or if you're entering the Cisco certification ecosystem for the first time. Budget 8–12 hours per week of focused study.
Official resources
Start with the Cisco Learning Network exam topics page for the authoritative blueprint. Cisco's free training catalog hosts an introductory AI learning path that aligns closely with the 810-110 blueprint and is the fastest way to spot which terms you don't recognize yet.