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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q35-Q40):

NEW QUESTION # 35
What is the primary goal of Retrieval-Augmented Generation (RAG) in AI systems?

Answer: A

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 # 36
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: B

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 # 37
Which approach protects PII when utilizing a public AI service for data analysis?

Answer: C

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.


NEW QUESTION # 38
What is a use case for generative AI in the testing phase of the software development lifecycle?

Answer: D

Explanation:
Generative AI can create synthetic test data that simulates realistic inputs and edge cases, helping validate application behavior during the testing phase without relying only on production data.


NEW QUESTION # 39
Which algorithm category is used when no labels exist in data?

Answer: B

Explanation:
Unsupervised learning discovers patterns or groupings without labeled outputs.


NEW QUESTION # 40
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