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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q18-Q23):

NEW QUESTION # 18
What is aggregation bias in the context of AI-assisted data analysis?

Answer: B

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


NEW QUESTION # 19
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

Answer: B

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.


NEW QUESTION # 20
A developer provides AI with three examples of a log entry mapped to a specific error category before asking AI to categorize a new log entry. Which technique is being used?

Answer: A

Explanation:
Few-shot prompting provides several examples of the expected input-output pattern so the AI can apply the same pattern to a new case.


NEW QUESTION # 21
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 # 22
Which activity occurs when a developer uses AI to generate a complete Python class for a new API client based on a high-level description?

Answer: B

Explanation:
Code scaffolding uses AI to generate the initial structure and boilerplate for a new component, such as a complete Python class for an API client based on a high-level description.


NEW QUESTION # 23
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