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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which Cisco technology is commonly associated with network observability and telemetry?

Answer: A

Explanation:
NetFlow provides visibility into traffic flows and is widely used for monitoring, analytics, and AI- driven operations.


NEW QUESTION # 61
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

Answer: B

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.


NEW QUESTION # 62
A team uses AI to scaffold a new microservice. What does this process involve?

Answer: D

Explanation:
Scaffolding uses AI to generate the initial project structure, boilerplate files, configuration, and basic code layout so the team can start development faster.


NEW QUESTION # 63
What is the role of the client in the Model Context Protocol?

Answer: D


NEW QUESTION # 64
Why is defining a persona considered a best practice for technical prompt engineering?

Answer: A

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.


NEW QUESTION # 65
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