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Cisco AI Technical Practitioner : 810-110

810-110

Exam Code: 810-110

Exam Name: Cisco AI Technical Practitioner

Updated: Aug 04, 2026

Q & A: 73 Questions and Answers

810-110 Free Demo download

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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering15%- Security and safety
  • 1. Defensive prompting methods
  • 2. Improving response accuracy
  • 3. Reducing hallucinations
  • 4. Prompt injection attacks
- Prompt structures
  • 1. Image generation
  • 2. Audio generation
  • 3. Text generation
- Principles and patterns
  • 1. Instructions
  • 2. Constraints
  • 3. Roles
- Prompting techniques
  • 1. Iterative prompting
  • 2. Few-shot prompting
  • 3. Chained prompting
Data Research and Analysis- Data analysis techniques
  • 1. Statistical analysis
  • 2. Data visualization
- Data collection and preparation
  • 1. Data gathering methodologies
  • 2. Data cleaning and preprocessing
AI Ethics and Security- Security threats and mitigations
  • 1. Model security
  • 2. Data privacy
- Ethical considerations in AI
  • 1. Transparency and explainability
  • 2. Bias and fairness
Agentic AI20%- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
Generative AI Models20%- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Context windows and token management
  • 1. Token limits
  • 2. Response management
- Common use cases
  • 1. Code generation
  • 2. Text summarization
  • 3. Content creation
- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Grounding AI outputs in external knowledge
  • 3. Embeddings
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations

Cisco AI Technical Practitioner Sample Questions:

1. Which activity occurs when a developer uses AI to generate a complete Python class for a new API client based on a high-level description?

A) implementing predictive function logic
B) performing code scaffolding
C) executing legacy code refactoring
D) providing inline co-pilot assistance


2. Which Cisco solution helps automate network operations using AI insights?

A) Cisco Hub
B) Cisco DNA Center
C) Cisco BIOS Manager
D) Cisco Repeater Utility


3. A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

A) correlate
B) visualize
C) orient
D) hypothesize


4. How does a coordinator agent function in a multi-agent AI framework?

A) It fine-tunes each agent's model weights to optimize performance on assigned tasks.
B) It orchestrates task delegation across specialized agents and aggregates the outputs.
C) It replaces the need for multiple agents by handling tasks within a single reasoning loop.
D) It serves as the external data source that each agent queries for grounding information.


5. What is the role of embeddings in AI systems?

A) Assigning IP addresses
B) Encrypting traffic
C) Compressing logs
D) Representing data numerically in vector space


Solutions:

Question # 1
Answer: B
Question # 2
Answer: B
Question # 3
Answer: D
Question # 4
Answer: B
Question # 5
Answer: D

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