810-110 Question Explanations & Frenquent 810-110 Update

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Cisco 810-110 Exam Syllabus Topics:

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

>> 810-110 Question Explanations <<

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Cisco AI Technical Practitioner Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which Cisco AI capability helps detect abnormal network behavior?

Answer: B

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


NEW QUESTION # 46
What is the purpose of tokenization in Large Language Models (LLMs)?

Answer: C

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.


NEW QUESTION # 47
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?

Answer: D

Explanation:
In the requirements phase, AI can analyze stakeholder documentation to identify logical gaps or inconsistencies and help convert business needs into structured user stories.


NEW QUESTION # 48
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

Answer: A

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.


NEW QUESTION # 49
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

Answer: A

Explanation:
RAG combines a large language model with external data retrieval systems so the model can answer using updated and domain-specific knowledge.


NEW QUESTION # 50
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