Pass Guaranteed 2026 810-110: Cisco AI Technical Practitioner–Reliable Actual Questions

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Cisco 810-110 Exam Syllabus Topics:

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

>> 810-110 Actual Questions <<

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Cisco AI Technical Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
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: D

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 # 66
What is hallucination in generative AI?

Answer: C

Explanation:
Hallucination occurs when an AI confidently produces false or misleading outputs.


NEW QUESTION # 67
Which concept helps reduce overfitting in machine learning?

Answer: C

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


NEW QUESTION # 68
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

Answer: B

Explanation:
Implementing constraints, such as maximum iteration limits, stop conditions, and execution boundaries, helps prevent an autonomous agent from repeatedly running actions without reaching a valid endpoint.


NEW QUESTION # 69
Which factor should be considered when selecting a pre-trained model for a mobile application with limited local computational resources?

Answer: C

Explanation:
Parameter count affects model size, memory usage, and compute requirements, so a smaller model is usually more suitable for a mobile application with limited local resources.


NEW QUESTION # 70
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