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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Agentic AI20%- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
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
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
Generative AI Models20%- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Embeddings
  • 3. Grounding AI outputs in external knowledge
- 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
- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Common use cases
  • 1. Text summarization
  • 2. Content creation
  • 3. Code generation
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%- Principles and patterns
  • 1. Roles
  • 2. Instructions
  • 3. Constraints
- Security and safety
  • 1. Reducing hallucinations
  • 2. Defensive prompting methods
  • 3. Prompt injection attacks
  • 4. Improving response accuracy
- Prompt structures
  • 1. Text generation
  • 2. Audio generation
  • 3. Image generation
- Prompting techniques
  • 1. Iterative prompting
  • 2. Chained prompting
  • 3. Few-shot prompting

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Cisco AI Technical Practitioner Sample Questions (Q53-Q58):

NEW QUESTION # 53
Why are prompts important in generative AI systems?

Answer: C

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.


NEW QUESTION # 54
How does a coordinator agent function in a multi-agent AI framework?

Answer: A

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 # 55
A developer provides AI with three examples of a log entry mapped to a specific error category before asking AI to categorize a new log entry. Which technique is being used?

Answer: C

Explanation:
Few-shot prompting provides several examples of the expected input-output pattern so the AI can apply the same pattern to a new case.


NEW QUESTION # 56
Which Cisco AI capability helps detect abnormal network behavior?

Answer: B

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


NEW QUESTION # 57
A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?

Answer: B

Explanation:
Prompt chaining breaks a complex task into a sequence of connected prompts, where each response becomes part of the input for the next step.


NEW QUESTION # 58
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