Review Key Concepts With 810-110 Exam-Preparation Questions

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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q19-Q24):

NEW QUESTION # 19
Which AI concern focuses on protecting sensitive information?

Answer: B

Explanation:
AI systems must ensure sensitive data is securely handled and protected.


NEW QUESTION # 20
Which Cisco technology is commonly associated with network observability and telemetry?

Answer: D

Explanation:
NetFlow provides visibility into traffic flows and is widely used for monitoring, analytics, and AI- driven operations.


NEW QUESTION # 21
A practitioner is using AI to analyze customer support tickets and usage data to compare customer complaint patterns across segments. According to the 4-step EDA methodology, what is the next action after generating a high-level summary of the feedback?

Answer: B

Explanation:
In the 4-step EDA methodology, after orienting with a high-level summary, the next step is to form hypotheses about possible patterns or relationships in the data, such as why complaint patterns differ across customer segments.


NEW QUESTION # 22
What is the purpose of tokenization in Large Language Models (LLMs)?

Answer: B

Explanation:
Tokenization converts text into smaller units (tokens) that AI models can process.


NEW QUESTION # 23
A practitioner is estimating the operational cost of integrating a cloud-hosted LLM API into an application. How does tokenization influence the cost of using this API?

Answer: C

Explanation:
Cloud-hosted LLM APIs commonly charge based on the number of input and output tokens processed, so tokenization directly affects the per-request cost.


NEW QUESTION # 24
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