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Cisco 810-110 Exam Syllabus Topics:

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

>> 810-110 Latest Exam Notes <<

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Cisco AI Technical Practitioner Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is a corporate data privacy risk when using a public AI chatbot?

Answer: D

Explanation:
A public AI chatbot may retain submitted corporate data or use it to improve model training, which can expose confidential, proprietary, or regulated information outside the organization's control.


NEW QUESTION # 12
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?

Answer: B

Explanation:
Diffusion models generate high-fidelity images by starting from noise and iteratively denoising it over multiple steps until a coherent image is produced.


NEW QUESTION # 13
Which Cisco platform provides AI-powered security analytics?

Answer: D

Explanation:
Cisco SecureX integrates security visibility, analytics, and automation using AI-driven insights.


NEW QUESTION # 14
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

Answer: A

Explanation:
A format-agnostic strategy keeps the content independent of a specific final layout, making it easier to reuse or adapt the same proposal material for different formats such as email, slides, documents, or reports.


NEW QUESTION # 15
Which Cisco solution helps automate network operations using AI insights?

Answer: A

Explanation:
Cisco DNA Center uses AI and analytics for intent-based networking and automation.


NEW QUESTION # 16
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