Cisco 810-110 Exam : Cisco AI Technical Practitioner

Cisco 810-110 exam
  • Exam Code: 810-110
  • Exam Name: Cisco AI Technical Practitioner
  • Updated: Aug 25, 2026
  • Q & A: 73 Questions and Answers
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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Generative AI Models20%- Model selection
  • 1. Selection from AI hubs
  • 2. Reasoning and multimodal tasks
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
- Retrieval Augmented Generation (RAG)
  • 1. Embeddings
  • 2. Vector databases
  • 3. Grounding AI outputs in external knowledge
Topic 2: Data Research and Analysis- Data analysis techniques
  • 1. Statistical analysis
  • 2. Data visualization
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
Topic 3: AI for Code and Workflow Optimization- Workflow automation
  • 1. Task optimization
  • 2. Process automation
- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation
Topic 4: Prompt Engineering15%- Security and safety
  • 1. Improving response accuracy
  • 2. Defensive prompting methods
  • 3. Prompt injection attacks
  • 4. Reducing hallucinations
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Principles and patterns
  • 1. Instructions
  • 2. Roles
  • 3. Constraints
- Prompting techniques
  • 1. Iterative prompting
  • 2. Chained prompting
  • 3. Few-shot prompting
Topic 5: Agentic AI20%- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
Topic 6: AI Ethics and Security- Security threats and mitigations
  • 1. Data privacy
  • 2. Model security
- Ethical considerations in AI
  • 1. Bias and fairness
  • 2. Transparency and explainability

Cisco AI Technical Practitioner Sample Questions:

1. A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?

A) bias mitigation
B) system safety
C) transparency
D) inclusiveness


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

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


3. Which Cisco platform provides AI-powered security analytics?

A) Cisco UCS Manager only
B) Cisco SecureX
C) Cisco IOS ROMMON
D) Cisco Packet Tracer


4. What is fine-tuning in generative AI?

A) Formatting storage drives
B) Replacing CPUs
C) Encrypting prompts
D) Training a pre-trained model on specialized data


5. How does context window management help control the cost of a long-running AI conversation?

A) by increasing the context window size to reduce the total number of API calls
B) by automatically switching to a lower-cost model as the conversation history grows
C) by capping the cumulative number of input tokens sent with each new prompt
D) by switching to an algorithm that reduces the character-to-token ratio


Solutions:

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

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