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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
What is fine-tuning in generative AI?

Answer: D

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


NEW QUESTION # 50
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

Answer: A

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.


NEW QUESTION # 51
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

Answer: D

Explanation:
Knowledge cut-off occurs when a model lacks information about events, standards, or specifications released after its training data period, causing it to provide outdated information.


NEW QUESTION # 52
What is the role of embeddings in AI systems?

Answer: B


NEW QUESTION # 53
Which concept helps reduce overfitting in machine learning?

Answer: C

Explanation:
Regularization techniques help models generalize better and avoid memorizing training data.


NEW QUESTION # 54
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