Three in-Demand Cisco 810-110 Exam Questions Formats

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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering15%- Security and safety
  • 1. Improving response accuracy
  • 2. Prompt injection attacks
  • 3. Reducing hallucinations
  • 4. Defensive prompting methods
- Principles and patterns
  • 1. Roles
  • 2. Constraints
  • 3. Instructions
- Prompting techniques
  • 1. Chained prompting
  • 2. Iterative prompting
  • 3. Few-shot prompting
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
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%- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
- Retrieval Augmented Generation (RAG)
  • 1. Embeddings
  • 2. Vector databases
  • 3. Grounding AI outputs in external knowledge
Agentic AI20%- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
AI for Code and Workflow Optimization- Workflow automation
  • 1. Task optimization
  • 2. Process automation
- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation
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 (Q63-Q68):

NEW QUESTION # 63
Which Cisco platform provides AI-powered security analytics?

Answer: B

Explanation:
Cisco SecureX integrates security visibility, analytics, and automation using AI-driven insights.


NEW QUESTION # 64
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 # 65
Which protocol is commonly used for streaming telemetry in modern networks?

Answer: C

Explanation:
gRPC supports efficient real-time telemetry streaming in modern network automation environments.


NEW QUESTION # 66
What is a corporate data privacy risk when using a public AI chatbot?

Answer: A

Explanation:
A public AI chatbot may retain submitted corporate data or use it to improve model training, which can expose confidential, proprietary, or regulated information outside the organization's control.


NEW QUESTION # 67
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 # 68
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