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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q52-Q57):

NEW QUESTION # 52
What does NLP stand for in AI?

Answer: C

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


NEW QUESTION # 53
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

Answer: B

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.


NEW QUESTION # 54
Which role does AI play in assisting with the refactoring of software code?

Answer: B

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 # 55
Which activity is an agentic AI use case compared to a standard generative AI use case?

Answer: A

Explanation:
Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.


NEW QUESTION # 56
In supervised machine learning, what is required?

Answer: D

Explanation:
Supervised learning trains models using labeled input-output pairs.


NEW QUESTION # 57
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