810-110參考資料 - 810-110考試資料

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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
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
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
Prompt Engineering15%- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Security and safety
  • 1. Reducing hallucinations
  • 2. Defensive prompting methods
  • 3. Prompt injection attacks
  • 4. Improving response accuracy
- Prompting techniques
  • 1. Chained prompting
  • 2. Iterative prompting
  • 3. Few-shot prompting
- Principles and patterns
  • 1. Instructions
  • 2. Roles
  • 3. Constraints
Generative AI Models20%- Context windows and token management
  • 1. Response management
  • 2. Token limits
- 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. Diffusion models
  • 2. Large language models (LLMs)
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
- Common use cases
  • 1. Code generation
  • 2. Content creation
  • 3. Text summarization
Data Research and Analysis- Data analysis techniques
  • 1. Data visualization
  • 2. Statistical analysis
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
Agentic AI20%- 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
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives

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Cisco 810-110考試資料,810-110最新考題

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最新的 Cisco AI Technical Practitioner (AITECH) 810-110 免費考試真題 (Q22-Q27):

問題 #22
Which Cisco solution helps automate network operations using AI insights?

答案:C

解題說明:
Cisco DNA Center uses AI and analytics for intent-based networking and automation.


問題 #23
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

答案:C

解題說明:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.


問題 #24
What is the purpose of tokenization in Large Language Models (LLMs)?

答案:C

解題說明:
Tokenization converts text into smaller units (tokens) that AI models can process.


問題 #25
Which role does AI play in assisting with the refactoring of software code?

答案:A

解題說明:
AI-assisted refactoring helps analyze existing code to identify design flaws, simplify structure, improve readability, and maintain behavior while making the code easier to maintain.


問題 #26
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

答案:A

解題說明:
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.


問題 #27
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