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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
Generative AI Models20%- Context windows and token management
  • 1. Token limits
  • 2. Response management
- Model hosting strategies
  • 1. Cloud-based vs. local deployments
  • 2. Cost, latency, privacy, and scalability considerations
- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Embeddings
  • 3. Grounding AI outputs in external knowledge
- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
Agentic AI20%- Differentiation from Generative AI
  • 1. Autonomous capabilities
  • 2. Use cases comparison
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
- Model Context Protocol (MCP)
  • 1. Context in agentic AI
  • 2. MCP framework primitives
- Data transformation and mapping
  • 1. Within AI Agents
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
Data Research and Analysis- Data analysis techniques
  • 1. Statistical analysis
  • 2. Data visualization
- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
Prompt Engineering15%- Security and safety
  • 1. Improving response accuracy
  • 2. Reducing hallucinations
  • 3. Defensive prompting methods
  • 4. Prompt injection attacks
- Principles and patterns
  • 1. Constraints
  • 2. Roles
  • 3. Instructions
- Prompting techniques
  • 1. Chained prompting
  • 2. Iterative prompting
  • 3. Few-shot prompting
- Prompt structures
  • 1. Text generation
  • 2. Image generation
  • 3. Audio generation
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

>> Clearer 810-110 Explanation <<

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Cisco AI Technical Practitioner Sample Questions (Q17-Q22):

NEW QUESTION # 17
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: A

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 # 18
Which AI model type is best suited for image recognition tasks?

Answer: A

Explanation:
CNNs are optimized for image and visual pattern recognition.


NEW QUESTION # 19
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

Answer: C

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.


NEW QUESTION # 20
Which workflow mechanism is being used when a company implements a policy where an AI agent can draft a customer email, but a human must authorize the transmission?

Answer: B

Explanation:
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.


NEW QUESTION # 21
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

Answer: C

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.


NEW QUESTION # 22
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