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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q72-Q77):

NEW QUESTION # 72
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 # 73
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?

Answer: D

Explanation:
Human-in-the-Loop governance ensures that a qualified person reviews, validates, and can override AI-generated decisions when outcomes may significantly affect people, such as medical diagnoses, treatment decisions, credit approvals, or financial risk assessments.


NEW QUESTION # 74
Which metric is commonly used to evaluate classification models?

Answer: B

Explanation:
Accuracy measures how many predictions were correct out of total predictions.


NEW QUESTION # 75
What does NLP stand for in AI?

Answer: D

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


NEW QUESTION # 76
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

Answer: D

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.


NEW QUESTION # 77
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