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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
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
Prompt Engineering15%- Principles and patterns
  • 1. Instructions
  • 2. Roles
  • 3. Constraints
- Prompting techniques
  • 1. Chained prompting
  • 2. Few-shot prompting
  • 3. Iterative prompting
- Prompt structures
  • 1. Text generation
  • 2. Image generation
  • 3. Audio generation
- Security and safety
  • 1. Improving response accuracy
  • 2. Prompt injection attacks
  • 3. Reducing hallucinations
  • 4. Defensive prompting methods
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
Generative AI Models20%- Retrieval Augmented Generation (RAG)
  • 1. Embeddings
  • 2. Grounding AI outputs in external knowledge
  • 3. Vector databases
- Common use cases
  • 1. Content creation
  • 2. Code generation
  • 3. Text summarization
- Context windows and token management
  • 1. Token limits
  • 2. Response management
- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- 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
Agentic AI20%- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Orchestration
  • 2. Human-in-the-loop (HITL) strategies
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
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

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Cisco AI Technical Practitioner Sample Questions (Q45-Q50):

NEW QUESTION # 45
What is the purpose of tokenization in Large Language Models (LLMs)?

Answer: D

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


NEW QUESTION # 46
Which concept helps reduce overfitting in machine learning?

Answer: D

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


NEW QUESTION # 47
How does AI support exploratory data analysis when working with large, unstructured datasets?

Answer: D

Explanation:
AI supports exploratory data analysis by detecting patterns, clusters, themes, and relationships in large unstructured datasets, helping practitioners understand the data before deeper analysis.


NEW QUESTION # 48
Which role does AI play in assisting with the refactoring of software code?

Answer: C

Explanation:
AI-assisted refactoring helps analyze existing code to identify design flaws, simplify structure, improve readability, and maintain behavior while making the code easier to maintain.


NEW QUESTION # 49
Which activity is an agentic AI use case compared to a standard generative AI use case?

Answer: C

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 # 50
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