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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Generative AI Models20%- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Grounding AI outputs in external knowledge
  • 3. Embeddings
- Context windows and token management
  • 1. Token limits
  • 2. Response management
- Model selection
  • 1. Selection from AI hubs
  • 2. Reasoning and multimodal tasks
- 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
- Common use cases
  • 1. Code generation
  • 2. Content creation
  • 3. Text summarization
Prompt Engineering15%- Prompting techniques
  • 1. Iterative prompting
  • 2. Few-shot prompting
  • 3. Chained prompting
- Principles and patterns
  • 1. Constraints
  • 2. Instructions
  • 3. Roles
- Security and safety
  • 1. Improving response accuracy
  • 2. Defensive prompting methods
  • 3. Prompt injection attacks
  • 4. Reducing hallucinations
- Prompt structures
  • 1. Audio generation
  • 2. Image generation
  • 3. Text generation
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
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
Agentic AI20%- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
AI Ethics and Security- Ethical considerations in AI
  • 1. Bias and fairness
  • 2. Transparency and explainability
- Security threats and mitigations
  • 1. Data privacy
  • 2. Model security

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810-110 Valid Test Pdf | 810-110 Valid Exam Answers

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Cisco AI Technical Practitioner Sample Questions (Q56-Q61):

NEW QUESTION # 56
Why are embeddings essential to the retrieval phase of a RAG system?

Answer: C

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 # 57
A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?

Answer: D

Explanation:
Transparency means clearly informing users when AI is being used, what it is doing, and how it may affect their data or experience.


NEW QUESTION # 58
How does AI support exploratory data analysis when working with large, unstructured datasets?

Answer: C

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.


NEW QUESTION # 59
What is a use case for generative AI in the testing phase of the software development lifecycle?

Answer: D

Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.


NEW QUESTION # 60
Why are prompts important in generative AI systems?

Answer: A

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


NEW QUESTION # 61
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