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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: Generative AI Models20%- Common use cases
  • 1. Text summarization
  • 2. Content creation
  • 3. Code generation
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- 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
- Major generative model families
  • 1. Diffusion models
  • 2. Large language models (LLMs)
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
Topic 3: AI Ethics and Security- Ethical considerations in AI
  • 1. Transparency and explainability
  • 2. Bias and fairness
- Security threats and mitigations
  • 1. Data privacy
  • 2. Model security
Topic 4: Agentic AI20%- Differentiation from Generative AI
  • 1. Use cases comparison
  • 2. Autonomous capabilities
- AI agent design principles
  • 1. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
- Data transformation and mapping
  • 1. Within AI Agents
Topic 5: Prompt Engineering15%- Security and safety
  • 1. Reducing hallucinations
  • 2. Prompt injection attacks
  • 3. Improving response accuracy
  • 4. Defensive prompting methods
- Prompt structures
  • 1. Audio generation
  • 2. Text generation
  • 3. Image generation
- Prompting techniques
  • 1. Iterative prompting
  • 2. Few-shot prompting
  • 3. Chained prompting
- Principles and patterns
  • 1. Constraints
  • 2. Roles
  • 3. Instructions
Topic 6: 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

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Cisco AI Technical Practitioner Sample Questions (Q61-Q66):

NEW QUESTION # 61
What is hallucination in generative AI?

Answer: C

Explanation:
Hallucination occurs when an AI confidently produces false or misleading outputs.


NEW QUESTION # 62
An organization has regulatory requirements that prohibit any proprietary source code from leaving the internal network. Which hosting strategy provides the highest level of data sovereignty?

Answer: D

Explanation:
Local LLM deployment on internal hardware keeps proprietary source code within the organization's internal network, providing the strongest control over data location, access, and governance.


NEW QUESTION # 63
Which concept helps reduce overfitting in machine learning?

Answer: C

Explanation:
Regularization techniques help models generalize better and avoid memorizing training data.


NEW QUESTION # 64
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

Answer: B

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 # 65
Why are prompts important in generative AI systems?

Answer: D

Explanation:
Well-structured prompts significantly improve response accuracy and relevance.


NEW QUESTION # 66
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