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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Agentic AI20%- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities
- 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. Context in agentic AI
  • 2. MCP framework primitives
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
Prompt Engineering15%- Security and safety
  • 1. Reducing hallucinations
  • 2. Prompt injection attacks
  • 3. Defensive prompting methods
  • 4. Improving response accuracy
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Chained prompting
  • 3. Iterative prompting
- Principles and patterns
  • 1. Constraints
  • 2. Roles
  • 3. Instructions
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
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
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
Generative AI Models20%- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Common use cases
  • 1. Content creation
  • 2. Text summarization
  • 3. Code generation
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Embeddings
  • 3. Grounding AI outputs in external knowledge

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Cisco AI Technical Practitioner Sample Questions (Q34-Q39):

NEW QUESTION # 34
What is the benefit of AI Ops (AIOps) in networking?

Answer: B

Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.


NEW QUESTION # 35
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 # 36
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

Answer: C

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.


NEW QUESTION # 37
What is the role of the client in the Model Context Protocol?

Answer: A

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.


NEW QUESTION # 38
How does a coordinator agent function in a multi-agent AI framework?

Answer: D

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.


NEW QUESTION # 39
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