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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q51-Q56):

NEW QUESTION # 51
An analyst needs a model that can analyze a network topology diagram and a set of configuration logs simultaneously. Which type of model should be selected?

Answer: D

Explanation:
A multimodal model can process and reason over different input types at the same time, such as images from a network topology diagram and text from configuration logs.


NEW QUESTION # 52
Which concept helps reduce overfitting in machine learning?

Answer: A

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


NEW QUESTION # 53
Which approach protects PII when utilizing a public AI service for data analysis?

Answer: B

Explanation:
Anonymizing or redacting sensitive personal information before sending data to a public AI service prevents PII from being exposed outside the organization's controlled environment.


NEW QUESTION # 54
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 # 55
What is fine-tuning in generative AI?

Answer: C

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.


NEW QUESTION # 56
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