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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q32-Q37):

NEW QUESTION # 32
What is aggregation bias in the context of AI-assisted data analysis?

Answer: A

Explanation:
Aggregation bias occurs when summarized or combined data hides meaningful differences across subgroups, causing important subpatterns to be missed.


NEW QUESTION # 33
What is the role of embeddings in AI systems?

Answer: D


NEW QUESTION # 34
What is fine-tuning in generative AI?

Answer: B

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.


NEW QUESTION # 35
What is the role of embeddings in AI systems?

Answer: D

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


NEW QUESTION # 36
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

Answer: C

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 # 37
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