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Cisco 810-110 Exam Syllabus Topics:

SectionWeightObjectives
AI for Code and Workflow Optimization- Workflow automation
  • 1. Task optimization
  • 2. Process automation
- AI-assisted code development
  • 1. Code generation
  • 2. Code review and debugging
Prompt Engineering15%- Security and safety
  • 1. Reducing hallucinations
  • 2. Prompt injection attacks
  • 3. Defensive prompting methods
  • 4. Improving response accuracy
- Prompt structures
  • 1. Text generation
  • 2. Image generation
  • 3. Audio generation
- Prompting techniques
  • 1. Few-shot prompting
  • 2. Iterative prompting
  • 3. Chained prompting
- Principles and patterns
  • 1. Constraints
  • 2. Instructions
  • 3. Roles
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
Generative AI Models20%- Model selection
  • 1. Selection from AI hubs
  • 2. Reasoning and multimodal tasks
- Common use cases
  • 1. Text summarization
  • 2. Code generation
  • 3. Content creation
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Major generative model families
  • 1. Large language models (LLMs)
  • 2. Diffusion models
- Retrieval Augmented Generation (RAG)
  • 1. Vector databases
  • 2. Grounding AI outputs in external knowledge
  • 3. Embeddings
- Model hosting strategies
  • 1. Cost, latency, privacy, and scalability considerations
  • 2. Cloud-based vs. local deployments
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
Agentic AI20%- 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
- Data transformation and mapping
  • 1. Within AI Agents

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Cisco AI Technical Practitioner Sample Questions (Q37-Q42):

NEW QUESTION # 37
Which concept helps reduce overfitting in machine learning?

Answer: A

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


NEW QUESTION # 38
How does AI support exploratory data analysis when working with large, unstructured datasets?

Answer: A

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 # 39
Which approach protects PII when utilizing a public AI service for data analysis?

Answer: D

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 # 40
Which metric is commonly used to evaluate classification models?

Answer: D

Explanation:
Accuracy measures how many predictions were correct out of total predictions.


NEW QUESTION # 41
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 # 42
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