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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Prompt Engineering15%- Principles and patterns
  • 1. Roles
  • 2. Instructions
  • 3. Constraints
- Prompt structures
  • 1. Image generation
  • 2. Text generation
  • 3. Audio generation
- Security and safety
  • 1. Defensive prompting methods
  • 2. Prompt injection attacks
  • 3. Reducing hallucinations
  • 4. Improving response accuracy
- Prompting techniques
  • 1. Chained prompting
  • 2. Iterative prompting
  • 3. Few-shot prompting
Generative AI Models20%- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Model selection
  • 1. Selection from AI hubs
  • 2. Reasoning and multimodal tasks
- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Embeddings
  • 3. Grounding AI outputs in external knowledge
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Common use cases
  • 1. Content creation
  • 2. Text summarization
  • 3. Code generation
Agentic AI20%- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
- AI agent design principles
  • 1. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
- Data transformation and mapping
  • 1. Within AI Agents
AI Ethics and Security- Ethical considerations in AI
  • 1. Transparency and explainability
  • 2. Bias and fairness
- Security threats and mitigations
  • 1. Model security
  • 2. Data privacy
AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation
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 (Q14-Q19):

NEW QUESTION # 14
Which AI-assisted task standardizes inconsistent date formats (e.g., MM/DD/YY and DD-MM- YYYY)?

Answer: C

Explanation:
Data transformation converts inconsistent data into a consistent format, such as standardizing multiple date formats into one required representation.


NEW QUESTION # 15
Which action demonstrates the principle of fairness in a generative AI deployment?

Answer: B

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.


NEW QUESTION # 16
What is the role of the client in the Model Context Protocol?

Answer: B


NEW QUESTION # 17
What is the benefit of AI Ops (AIOps) in networking?

Answer: B

Explanation:
AIOps uses AI to improve IT operations, detect anomalies, and accelerate troubleshooting.


NEW QUESTION # 18
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?

Answer: D

Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.


NEW QUESTION # 19
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