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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q36-Q41):

NEW QUESTION # 36
Which approach protects PII when utilizing a public AI service for data analysis?

Answer: C

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.


NEW QUESTION # 37
Which algorithm category is used when no labels exist in data?

Answer: D

Explanation:
Unsupervised learning discovers patterns or groupings without labeled outputs.


NEW QUESTION # 38
What is the purpose of tokenization in Large Language Models (LLMs)?

Answer: A

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.


NEW QUESTION # 39
What is the role of an MCP server in the MCP open standard?

Answer: A

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.


NEW QUESTION # 40
In an AI-powered automation workflow, what is the function of an approval gate?

Answer: C

Explanation:
An approval gate adds human review before an automated workflow performs a high-impact action, helping ensure that AI-driven decisions are checked for correctness, risk, and business impact before execution.


NEW QUESTION # 41
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