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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
When using AI to draft a technical proposal, what is the benefit of a format-agnostic strategy?

Answer: B

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 # 66
What is the role of the client in the Model Context Protocol?

Answer: C


NEW QUESTION # 67
Which Cisco AI capability helps detect abnormal network behavior?

Answer: C

Explanation:
AI systems can identify unusual patterns and potential threats automatically.


NEW QUESTION # 68
Which generative AI model family should be used for identifying and summarizing technical themes across thousands of unstructured network incident reports?

Answer: C

Explanation:
Large language models are designed to process and generate natural language, making them suitable for identifying recurring technical themes and summarizing large volumes of unstructured incident reports.


NEW QUESTION # 69
What is the main advantage of cloud-based AI services?

Answer: C

Explanation:
Cloud AI platforms provide scalable infrastructure and elastic computing power for AI workloads.


NEW QUESTION # 70
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