Cisco AI Technical Practitioner: 810-110 Exam

"Cisco AI Technical Practitioner", also known as 810-110 exam, is a Cisco Certification. With the complete collection of questions and answers, UpdateDumps has assembled to take you through 73 Q&As to your 810-110 Exam preparation. In the 810-110 exam resources, you will cover every field and category in Cisco AI Technical Practitioner (AITECH) Certification helping to ready you for your successful Cisco Certification.

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Cisco 810-110 Exam Syllabus Topics:

Section	Weight	Objectives
Prompt Engineering	15%	- Prompting techniques 1. Chained prompting 2. Iterative prompting 3. Few-shot prompting - Principles and patterns 1. Instructions 2. Constraints 3. Roles - Security and safety 1. Improving response accuracy 2. Reducing hallucinations 3. Defensive prompting methods 4. Prompt injection attacks - Prompt structures 1. Text generation 2. Audio generation 3. Image generation
AI Ethics and Security		- Ethical considerations in AI 1. Transparency and explainability 2. Bias and fairness - Security threats and mitigations 1. Data privacy 2. Model security
Data Research and Analysis		- Data analysis techniques 1. Data visualization 2. Statistical analysis - Data collection and preparation 1. Data gathering methodologies 2. Data cleaning and preprocessing
AI for Code and Workflow Optimization		- Workflow automation 1. Process automation 2. Task optimization - AI-assisted code development 1. Code review and debugging 2. Code generation
Generative AI Models	20%	- Common use cases 1. Text summarization 2. Code generation 3. Content creation - Model hosting strategies 1. Cost, latency, privacy, and scalability considerations 2. Cloud-based vs. local deployments - Retrieval Augmented Generation (RAG) 1. Grounding AI outputs in external knowledge 2. Embeddings 3. Vector databases - Model selection 1. Selection from AI hubs 2. Reasoning and multimodal tasks - Context windows and token management 1. Token limits 2. Response management - Major generative model families 1. Diffusion models 2. Large language models (LLMs)
Agentic AI	20%	- Data transformation and mapping 1. Within AI Agents - Differentiation from Generative AI 1. Autonomous capabilities 2. Use cases comparison - AI agent design principles 1. Human-in-the-loop (HITL) strategies 2. Orchestration - Model Context Protocol (MCP) 1. Context in agentic AI 2. MCP framework primitives

Cisco AI Technical Practitioner Sample Questions:

Question #1

Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

A. using a short system prompt
B. increasing the model creativity
C. removing negative constraints
D. requesting citations for the summary

Reveal Solution Discussion 0

Correct Answer: D 🗳️