Exam 810-110 Labs, 810-110 New Study Materials

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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
- Data transformation and mapping
  • 1. Within AI Agents
- AI agent design principles
  • 1. Human-in-the-loop (HITL) strategies
  • 2. Orchestration
- Model Context Protocol (MCP)
  • 1. MCP framework primitives
  • 2. Context in agentic AI
Topic 2: AI Ethics and Security- Ethical considerations in AI
  • 1. Transparency and explainability
  • 2. Bias and fairness
- Security threats and mitigations
  • 1. Model security
  • 2. Data privacy
Topic 3: AI for Code and Workflow Optimization- Workflow automation
  • 1. Process automation
  • 2. Task optimization
- AI-assisted code development
  • 1. Code review and debugging
  • 2. Code generation
Topic 4: Generative AI Models20%- Model selection
  • 1. Reasoning and multimodal tasks
  • 2. Selection from AI hubs
- Context windows and token management
  • 1. Response management
  • 2. Token limits
- Common use cases
  • 1. Code generation
  • 2. Text summarization
  • 3. Content creation
- Retrieval Augmented Generation (RAG)
  • 1. Grounding AI outputs in external knowledge
  • 2. Embeddings
  • 3. Vector databases
- 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 5: Prompt Engineering15%- Prompting techniques
  • 1. Iterative prompting
  • 2. Chained prompting
  • 3. Few-shot prompting
- Security and safety
  • 1. Defensive prompting methods
  • 2. Prompt injection attacks
  • 3. Reducing hallucinations
  • 4. Improving response accuracy
- Prompt structures
  • 1. Text generation
  • 2. Image generation
  • 3. Audio generation
- Principles and patterns
  • 1. Roles
  • 2. Instructions
  • 3. Constraints
Topic 6: Data Research and Analysis- Data collection and preparation
  • 1. Data cleaning and preprocessing
  • 2. Data gathering methodologies
- Data analysis techniques
  • 1. Data visualization
  • 2. Statistical analysis

>> Exam 810-110 Labs <<

810-110 New Study Materials | Exam 810-110 Prep

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Cisco AI Technical Practitioner Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which action demonstrates the principle of fairness in a generative AI deployment?

Answer: B

Explanation:
Fairness requires checking whether model outputs treat users or groups equitably, so auditing outputs for bias helps identify and reduce discriminatory or unequal outcomes.


NEW QUESTION # 57
Which activity is an agentic AI use case compared to a standard generative AI use case?

Answer: A

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 # 58
Which AI-driven activity involves a developer providing runtime error logs to a model to receive an explanation of the root cause and a suggested fix?

Answer: B

Explanation:
AI-assisted debugging uses error logs, stack traces, and runtime context to help identify the root cause of a failure and suggest a corrective fix.


NEW QUESTION # 59
What is an advantage of cloud-hosted API services when comparing cloud-hosted and locally hosted AI models?

Answer: B

Explanation:
Cloud-hosted API services can scale inference capacity quickly based on demand, making them suitable for workloads with unpredictable or fluctuating usage.


NEW QUESTION # 60
What is fine-tuning in generative AI?

Answer: A

Explanation:
Fine-tuning adapts a general AI model to specific domains or business use cases.


NEW QUESTION # 61
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