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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q51-Q56):

NEW QUESTION # 51
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

Answer: C

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.


NEW QUESTION # 52
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

Answer: C

Explanation:
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.


NEW QUESTION # 53
Why are prompts important in generative AI systems?

Answer: D

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.


NEW QUESTION # 54
What is the main advantage of cloud-based AI services?

Answer: C

Explanation:
Cloud AI platforms provide scalable infrastructure and elastic computing power for AI workloads.


NEW QUESTION # 55
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?

Answer: C

Explanation:
Diffusion models generate high-fidelity images by starting from noise and iteratively denoising it over multiple steps until a coherent image is produced.


NEW QUESTION # 56
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