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CISCO

Cisco AI Technical Practitioner

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.

Practice Free Questions

6Domains

27Objectives

131Practice pages

607Free questions

AI-TECHNICAL-PRACTITIONER free practice questions

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1.1 Describe major generative AI model families (e.g., LLMs, diffusion models) and common use cases (text summarization, content creation, code generation)

18 questions · 4 pages

Questions 1–5 6–10 11–15 16–18

1.2 Compare model hosting options (cloud-hosted vs locally hosted) and their trade-offs (cost, latency, privacy, scalability)

10 questions · 2 pages

Questions 1–5 6–10

1.3 Explain role of context windows, token limits and response management

22 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–22

1.4 Understand model selection in AI model hubs and repositories for appropriate use‑cases (e.g., reasoning, multimodality)

18 questions · 4 pages

Questions 1–5 6–10 11–15 16–18

1.5 Describe Retrieval Augmented Generation (RAG) and role of embeddings and vector databases

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

5.1 Describe AI's role across the software development lifecycle (requirements, prototyping, implementation, testing, deployment)

24 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–24

5.2 Describe the AI capabilities for code generation and rapid prototyping

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

5.3 Explain AI workflow design and monitoring principles

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

5.4 Describe how token usage and context‑window management affect prototyping cost, latency, and output quality

27 questions · 6 pages

Questions 1–5 6–10 11–15 16–20 21–25 26–27

5.5 Explain how AI improves code quality (debugging assistance, error handling, documentation)

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

6.1 Differentiate Agentic AI from Generative AI use cases

16 questions · 4 pages

Questions 1–5 6–10 11–15 16–16

6.2 Explain AI agent design principles, autonomous capabilities, and orchestration

18 questions · 4 pages

Questions 1–5 6–10 11–15 16–18

6.3 Describe Model Context Protocol (MCP) framework primitives in context of agentic AI

23 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–23

6.4 Explain human-in-the-loop (HITL) strategies

21 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–21

6.5 Describe data transformation and mapping within AI Agents

25 questions · 5 pages

Questions 1–5 6–10 11–15 16–20 21–25

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Percentages reflect share of the current practice bank, not official exam weightings. Every page above is a live, crawlable practice page (27 objectives · 131 pages).