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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q49-Q54):

NEW QUESTION # 49
What is the purpose of a vector database in a RAG architecture?

Answer: D

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 # 50
A practitioner is solving a complex architectural problem by breaking it into four sequential prompts. Each prompt's output is fed as input into the next prompt in the sequence. Which prompt engineering technique is being used?

Answer: D

Explanation:
Prompt chaining breaks a complex task into a sequence of connected prompts, where each response becomes part of the input for the next step.


NEW QUESTION # 51
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

Answer: B

Explanation:
RAG combines a large language model with external data retrieval systems so the model can answer using updated and domain-specific knowledge.


NEW QUESTION # 52
Which Cisco AI capability helps detect abnormal network behavior?

Answer: B

Explanation:
AI systems can identify unusual patterns and potential threats automatically.


NEW QUESTION # 53
What is the role of embeddings in AI systems?

Answer: A


NEW QUESTION # 54
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