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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q47-Q52):

NEW QUESTION # 47
What is the role of the client in the Model Context Protocol?

Answer: C


NEW QUESTION # 48
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: B

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 # 49
What does NLP stand for in AI?

Answer: D

Explanation:
NLP enables computers to understand and generate human language.


NEW QUESTION # 50
How does a coordinator agent function in a multi-agent AI framework?

Answer: A

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 # 51
Which role does AI play in assisting with the refactoring of software code?

Answer: C

Explanation:
AI-assisted refactoring helps analyze existing code to identify design flaws, simplify structure, improve readability, and maintain behavior while making the code easier to maintain.


NEW QUESTION # 52
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