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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: Agentic AI20%- Data transformation and mapping
  • 1. Within AI Agents
- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities
- 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
Topic 3: Generative AI Models20%- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
- Model selection
  • 1. Selection from AI hubs
  • 2. Reasoning and multimodal tasks
- Common use cases
  • 1. Content creation
  • 2. Code generation
  • 3. Text summarization
- Retrieval Augmented Generation (RAG)
  • 1. Grounding AI outputs in external knowledge
  • 2. Vector databases
  • 3. Embeddings
- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Context windows and token management
  • 1. Token limits
  • 2. Response management
Topic 4: Prompt Engineering15%- Prompt structures
  • 1. Text generation
  • 2. Audio generation
  • 3. Image generation
- Principles and patterns
  • 1. Roles
  • 2. Instructions
  • 3. Constraints
- Prompting techniques
  • 1. Iterative prompting
  • 2. Chained prompting
  • 3. Few-shot prompting
- Security and safety
  • 1. Defensive prompting methods
  • 2. Prompt injection attacks
  • 3. Reducing hallucinations
  • 4. Improving response accuracy
Topic 5: Data Research and Analysis- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
- Data analysis techniques
  • 1. Data visualization
  • 2. Statistical analysis
Topic 6: 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 (Q66-Q71):

NEW QUESTION # 66
What is the role of embeddings in AI systems?

Answer: C

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


NEW QUESTION # 67
How does a coordinator agent function in a multi-agent AI framework?

Answer: D

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 # 68
Why are prompts important in generative AI systems?

Answer: A

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.


NEW QUESTION # 69
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

Answer: C

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.


NEW QUESTION # 70
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

Answer: A

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.


NEW QUESTION # 71
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