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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q52-Q57):

NEW QUESTION # 52
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

Answer: A

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.


NEW QUESTION # 53
What is a corporate data privacy risk when using a public AI chatbot?

Answer: D

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 # 54
What is hallucination in generative AI?

Answer: C

Explanation:
Hallucination occurs when an AI confidently produces false or misleading outputs.


NEW QUESTION # 55
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 # 56
What is a difference between generative AI and agentic AI?

Answer: B

Explanation:
Generative AI primarily creates content or responses from a prompt, while agentic AI can plan, reason, use tools, and execute multi-step workflows toward a goal.


NEW QUESTION # 57
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