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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q33-Q38):

NEW QUESTION # 33
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 # 34
What is the role of embeddings in AI systems?

Answer: B


NEW QUESTION # 35
What is the purpose of a vector database in a RAG architecture?

Answer: B

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 # 36
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

Answer: A

Explanation:
Token-based billing is driven by the number of input and output tokens processed, so reducing the prompt size lowers the total tokens consumed across high-volume summarization requests.


NEW QUESTION # 37
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: B

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 # 38
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