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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner 認定 810-110 試験問題 (Q12-Q17):

質問 # 12
Which Cisco AI capability helps detect abnormal network behavior?

正解：A

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


質問 # 13
What is the role of the client in the Model Context Protocol?

正解：C


質問 # 14
Which Cisco solution helps automate network operations using AI insights?

正解：D

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


質問 # 15
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?

正解：B

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


質問 # 16
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

正解：C

解説：
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.


質問 # 17
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