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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Research and Analysis- Data analysis techniques
  • 1. Data visualization
  • 2. Statistical analysis
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
Topic 2: AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
Topic 3: Prompt Engineering15%- Prompting techniques
  • 1. Few-shot prompting
  • 2. Chained prompting
  • 3. Iterative prompting
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Security and safety
  • 1. Improving response accuracy
  • 2. Prompt injection attacks
  • 3. Reducing hallucinations
  • 4. Defensive prompting methods
- Principles and patterns
  • 1. Constraints
  • 2. Roles
  • 3. Instructions
Topic 4: 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
Topic 5: Generative AI Models20%- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- 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
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
Topic 6: Agentic AI20%- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration

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Cisco AI Technical Practitioner Sample Questions (Q10-Q15):

NEW QUESTION # 10
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

Answer: C

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 # 11
Which activity is an agentic AI use case compared to a standard generative AI use case?

Answer: D

Explanation:
Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.


NEW QUESTION # 12
A marketing team must generate high-fidelity images for a new product launch. Which model is designed to handle this task through the process of iterative noise reduction?

Answer: C

Explanation:
Diffusion models generate high-fidelity images by starting from noise and iteratively denoising it over multiple steps until a coherent image is produced.


NEW QUESTION # 13
What is the purpose of tokenization in Large Language Models (LLMs)?

Answer: C

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.


NEW QUESTION # 14
Which Cisco AI capability helps detect abnormal network behavior?

Answer: B

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


NEW QUESTION # 15
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