810-110 Exam Certification - Latest 810-110 Test Guide

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Cisco 810-110 Exam Syllabus Topics:

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

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Latest Cisco 810-110 Test Guide & New 810-110 Test Practice

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Cisco AI Technical Practitioner Sample Questions (Q24-Q29):

NEW QUESTION # 24
A user includes project background information and the specific technical constraints in a prompt.
Which principle of prompt engineering is being applied?

Answer: C

Explanation:
Describing the situation means providing relevant context, background information, and constraints so the AI can generate a response that fits the specific scenario.


NEW QUESTION # 25
A practitioner uses self-consistency by running the same prompt three times and comparing the results. What is the goal of this strategy?

Answer: C

Explanation:
Self-consistency compares multiple responses to the same prompt to check whether the model produces stable, reliable results across repeated runs.


NEW QUESTION # 26
Why is defining a persona considered a best practice for technical prompt engineering?

Answer: A

Explanation:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.


NEW QUESTION # 27
Which Cisco platform provides AI-powered security analytics?

Answer: C

Explanation:
Cisco SecureX integrates security visibility, analytics, and automation using AI-driven insights.


NEW QUESTION # 28
How does a coordinator agent function in a multi-agent AI framework?

Answer: B

Explanation:
A coordinator agent manages the overall workflow by assigning tasks to specialized agents, coordinating their interactions, and combining their outputs into a final result.


NEW QUESTION # 29
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