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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: AI for Code and Workflow Optimization- Workflow automation
  • 1. Task optimization
  • 2. Process automation
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
Topic 3: Agentic AI20%- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- 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
Topic 4: 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
Topic 5: Prompt Engineering15%- Prompt structures
  • 1. Image generation
  • 2. Audio generation
  • 3. Text generation
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Chained prompting
  • 3. Iterative prompting
- Security and safety
  • 1. Improving response accuracy
  • 2. Reducing hallucinations
  • 3. Defensive prompting methods
  • 4. Prompt injection attacks
- Principles and patterns
  • 1. Roles
  • 2. Instructions
  • 3. Constraints
Topic 6: Generative AI Models20%- 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
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Context windows and token management
  • 1. Token limits
  • 2. Response management
- Common use cases
  • 1. Code generation
  • 2. Text summarization
  • 3. Content creation
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations

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Cisco AI Technical Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
What does NLP stand for in AI?

Answer: B

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


NEW QUESTION # 65
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?

Answer: D

Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.


NEW QUESTION # 66
In supervised machine learning, what is required?

Answer: A


NEW QUESTION # 67
What is the role of embeddings in AI systems?

Answer: B


NEW QUESTION # 68
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

Answer: B

Explanation:
Describing the situation means providing relevant context, background information, and constraints so the AI can generate a response that fits the specific scenario.


NEW QUESTION # 69
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