810-110 Exam Resources & 810-110 Best Questions & 810-110 Exam Dumps

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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q30-Q35):

NEW QUESTION # 30
Why is defining a persona considered a best practice for technical prompt engineering?

Answer: D

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.


NEW QUESTION # 31
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

Answer: D

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.


NEW QUESTION # 32
Which activity occurs when a developer uses AI to generate a complete Python class for a new API client based on a high-level description?

Answer: A

Explanation:
Code scaffolding uses AI to generate the initial structure and boilerplate for a new component, such as a complete Python class for an API client based on a high-level description.


NEW QUESTION # 33
Which Cisco platform provides AI-powered security analytics?

Answer: D

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


NEW QUESTION # 34
Which risk occurs when an AI model provides an outdated technical specification because it was trained before the new standard was released?

Answer: C

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 # 35
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