Exam Cisco 810-110 Exercise - Exam 810-110 Guide

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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q63-Q68):

NEW QUESTION # 63
What is the role of an MCP server in the MCP open standard?

Answer: D

Explanation:
An MCP server exposes external tools, data sources, and resources through a standardized interface so an AI model or agent can access capabilities beyond its built-in training data.


NEW QUESTION # 64
Which threat involves an attacker intentionally contaminating the training set of a model to influence its future behavior?

Answer: C

Explanation:
Data poisoning occurs when an attacker deliberately manipulates or contaminates training data so the model learns harmful, biased, or attacker-controlled behavior.


NEW QUESTION # 65
Which AI model type is best suited for image recognition tasks?

Answer: C

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


NEW QUESTION # 66
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

Answer: A

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 # 67
In an AI-powered automation workflow, what is the function of an approval gate?

Answer: D

Explanation:
An approval gate adds human review before an automated workflow performs a high-impact action, helping ensure that AI-driven decisions are checked for correctness, risk, and business impact before execution.


NEW QUESTION # 68
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