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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: Prompt Engineering15%- Prompt structures
  • 1. Text generation
  • 2. Audio generation
  • 3. Image generation
- Principles and patterns
  • 1. Instructions
  • 2. Constraints
  • 3. Roles
- Security and safety
  • 1. Improving response accuracy
  • 2. Prompt injection attacks
  • 3. Reducing hallucinations
  • 4. Defensive prompting methods
- Prompting techniques
  • 1. Iterative prompting
  • 2. Chained prompting
  • 3. Few-shot prompting
Topic 3: AI Ethics and Security- Ethical considerations in AI
  • 1. Transparency and explainability
  • 2. Bias and fairness
- Security threats and mitigations
  • 1. Model security
  • 2. Data privacy
Topic 4: Generative AI Models20%- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Retrieval Augmented Generation (RAG)
  • 1. Grounding AI outputs in external knowledge
  • 2. Vector databases
  • 3. Embeddings
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
- 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
Topic 5: Data Research and Analysis- Data analysis techniques
  • 1. Statistical analysis
  • 2. Data visualization
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
Topic 6: AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation

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Cisco AI Technical Practitioner Sample Questions (Q11-Q16):

NEW QUESTION # 11
What happens when the input provided to an LLM exceeds its context window limit?

Answer: C

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.


NEW QUESTION # 12
How does a coordinator agent function in a multi-agent AI framework?

Answer: B

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.


NEW QUESTION # 13
In supervised machine learning, what is required?

Answer: D


NEW QUESTION # 14
Which metric is commonly used to evaluate classification models?

Answer: D

Explanation:
Accuracy measures how many predictions were correct out of total predictions.


NEW QUESTION # 15
What is a use case for generative AI in the testing phase of the software development lifecycle?

Answer: B

Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.


NEW QUESTION # 16
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