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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q68-Q73):

NEW QUESTION # 68
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 # 69
What is the benefit of AI Ops (AIOps) in networking?

Answer: D

Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.


NEW QUESTION # 70
Which Cisco AI capability helps detect abnormal network behavior?

Answer: C

Explanation:
AI systems can identify unusual patterns and potential threats automatically.


NEW QUESTION # 71
Why are embeddings essential to the retrieval phase of a RAG system?

Answer: D

Explanation:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.


NEW QUESTION # 72
Which Cisco technology is commonly associated with network observability and telemetry?

Answer: C

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


NEW QUESTION # 73
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