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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
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
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
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. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
Prompt Engineering15%- Prompting techniques
  • 1. Few-shot prompting
  • 2. Iterative prompting
  • 3. Chained prompting
- Prompt structures
  • 1. Image generation
  • 2. Audio generation
  • 3. Text generation
- Principles and patterns
  • 1. Instructions
  • 2. Constraints
  • 3. Roles
- Security and safety
  • 1. Prompt injection attacks
  • 2. Defensive prompting methods
  • 3. Improving response accuracy
  • 4. Reducing hallucinations
AI for Code and Workflow Optimization- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
- Workflow automation
  • 1. Task optimization
  • 2. Process automation
Generative AI Models20%- Model selection
  • 1. Selection from AI hubs
  • 2. Reasoning and multimodal tasks
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
- Retrieval Augmented Generation (RAG)
  • 1. Grounding AI outputs in external knowledge
  • 2. Embeddings
  • 3. Vector databases
- 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
- Context windows and token management
  • 1. Token limits
  • 2. Response management

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Cisco AI Technical Practitioner Sample Questions (Q22-Q27):

NEW QUESTION # 22
Which concept helps reduce overfitting in machine learning?

Answer: D

Explanation:
Regularization techniques help models generalize better and avoid memorizing training data.


NEW QUESTION # 23
Which workflow mechanism is being used when a company implements a policy where an AI agent can draft a customer email, but a human must authorize the transmission?

Answer: B

Explanation:
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.


NEW QUESTION # 24
What is the role of an MCP server in the MCP open standard?

Answer: B

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.


NEW QUESTION # 25
Which AI concern focuses on protecting sensitive information?

Answer: C

Explanation:
AI systems must ensure sensitive data is securely handled and protected.


NEW QUESTION # 26
What is the role of embeddings in AI systems?

Answer: C


NEW QUESTION # 27
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