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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q51-Q56):

NEW QUESTION # 51
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 # 52
A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?

Answer: C

Explanation:
Prompt chaining breaks a complex task into a sequence of connected prompts, where each response becomes part of the input for the next step.


NEW QUESTION # 53
What is the role of the client in the Model Context Protocol?

Answer: A

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.


NEW QUESTION # 54
In supervised machine learning, what is required?

Answer: C


NEW QUESTION # 55
Which Cisco solution helps automate network operations using AI insights?

Answer: D

Explanation:
Cisco DNA Center uses AI and analytics for intent-based networking and automation.


NEW QUESTION # 56
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