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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Agentic AI20%- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities
- 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. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
Topic 2: Data Research and Analysis- Data analysis techniques
  • 1. Statistical analysis
  • 2. Data visualization
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
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: 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
Topic 5: Prompt Engineering15%- Prompting techniques
  • 1. Chained prompting
  • 2. Iterative prompting
  • 3. Few-shot prompting
- Principles and patterns
  • 1. Instructions
  • 2. Roles
  • 3. Constraints
- 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
Topic 6: Generative AI Models20%- Retrieval Augmented Generation (RAG)
  • 1. Grounding AI outputs in external knowledge
  • 2. Embeddings
  • 3. Vector databases
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Common use cases
  • 1. Text summarization
  • 2. Content creation
  • 3. Code generation
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations

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Cisco AI Technical Practitioner Sample Questions (Q43-Q48):

NEW QUESTION # 43
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: A

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 # 44
What happens when the input provided to an LLM exceeds its context window limit?

Answer: C

Explanation:
When input exceeds the context window, the model cannot process all provided text at once, so earlier content may be truncated or pushed out of the active context.


NEW QUESTION # 45
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

Answer: D

Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.


NEW QUESTION # 46
What is the role of embeddings in AI systems?

Answer: C

Explanation:
Embeddings convert text or objects into vectors that capture semantic meaning.


NEW QUESTION # 47
In an AI-powered automation workflow, what is the function of an approval gate?

Answer: A

Explanation:
An approval gate adds human review before an automated workflow performs a high-impact action, helping ensure that AI-driven decisions are checked for correctness, risk, and business impact before execution.


NEW QUESTION # 48
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