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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation
Topic 3: Generative AI Models20%- Retrieval Augmented Generation (RAG)
  • 1. Grounding AI outputs in external knowledge
  • 2. Embeddings
  • 3. Vector databases
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Common use cases
  • 1. Code generation
  • 2. Text summarization
  • 3. Content creation
- Context windows and token management
  • 1. Response management
  • 2. Token limits
Topic 4: 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
Topic 5: Prompt Engineering15%- Prompting techniques
  • 1. Iterative prompting
  • 2. Chained prompting
  • 3. Few-shot prompting
- Prompt structures
  • 1. Audio generation
  • 2. Image generation
  • 3. Text generation
- Security and safety
  • 1. Improving response accuracy
  • 2. Defensive prompting methods
  • 3. Reducing hallucinations
  • 4. Prompt injection attacks
- Principles and patterns
  • 1. Constraints
  • 2. Instructions
  • 3. Roles
Topic 6: Agentic AI20%- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
- Data transformation and mapping
  • 1. Within AI Agents
- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities

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Cisco AI Technical Practitioner Sample Questions (Q66-Q71):

NEW QUESTION # 66
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 # 67
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

Answer: A

Explanation:
Knowledge cut-off occurs when a model lacks information about events, standards, or specifications released after its training data period, causing it to provide outdated information.


NEW QUESTION # 68
What is the role of the client in the Model Context Protocol?

Answer: D

Explanation:
In the Model Context Protocol, the client manages communication between the AI application and MCP servers, coordinating requests for tools, resources, and context needed during task execution.


NEW QUESTION # 69
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: D

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 # 70
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 # 71
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