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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q61-Q66):

NEW QUESTION # 61
How does context window management help control the cost of a long-running AI conversation?

Answer: A

Explanation:
Context window management controls cost by limiting how much prior conversation history is included with each new request, reducing the number of input tokens processed and billed.


NEW QUESTION # 62
Which AI concern focuses on protecting sensitive information?

Answer: D

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


NEW QUESTION # 63
In supervised machine learning, what is required?

Answer: A

Explanation:
Supervised learning trains models using labeled input-output pairs.


NEW QUESTION # 64
What is the role of embeddings in AI systems?

Answer: B

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.


NEW QUESTION # 65
What is hallucination in generative AI?

Answer: D

Explanation:
Hallucination occurs when an AI confidently produces false or misleading outputs.


NEW QUESTION # 66
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