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Cisco 810-110 Exam Syllabus Topics:

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

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

NEW QUESTION # 72
Which Cisco AI capability helps detect abnormal network behavior?

Answer: C

Explanation:
AI systems can identify unusual patterns and potential threats automatically.


NEW QUESTION # 73
Which Cisco solution helps automate network operations using AI insights?

Answer: B

Explanation:
Cisco DNA Center uses AI and analytics for intent-based networking and automation.


NEW QUESTION # 74
What is the purpose of a vector database in a RAG architecture?

Answer: C

Explanation:
A vector database stores embeddings, which are numerical representations of data, so a RAG system can retrieve semantically relevant information for grounding the model's response.


NEW QUESTION # 75
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

Answer: B

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.


NEW QUESTION # 76
Why are embeddings essential to the retrieval phase of a RAG system?

Answer: D

Explanation:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.


NEW QUESTION # 77
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