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Cisco 810-110 Exam Syllabus Topics:

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

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Cisco AI Technical Practitioner Sample Questions (Q71-Q76):

NEW QUESTION # 71
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?

Answer: D

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 # 72
Which design principle helps prevent an autonomous agent from looping infinitely and wasting tokens?

Answer: C

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 # 73
What is hallucination in generative AI?

Answer: B

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


NEW QUESTION # 74
Which strategy should be used to mitigate hallucinations when asking an AI to summarize a specific technical document?

Answer: A

Explanation:
Requesting citations grounds the summary in the provided technical document and makes the model support its claims with specific source references, reducing the chance of unsupported or fabricated content.


NEW QUESTION # 75
Which activity is an agentic AI use case compared to a standard generative AI use case?

Answer: B

Explanation:
Agentic AI can plan and execute multi-step actions toward a goal, such as diagnosing a failed build, applying a fix, running validation, and iterating until the build succeeds.


NEW QUESTION # 76
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