810-110 Certification Guide Is Beneficial 810-110 Exam Guide Dump

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

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

NEW QUESTION # 56
A team uses AI to scaffold a new microservice. What does this process involve?

Answer: C

Explanation:
Scaffolding uses AI to generate the initial project structure, boilerplate files, configuration, and basic code layout so the team can start development faster.


NEW QUESTION # 57
A developer provides AI with three examples of a log entry mapped to a specific error category before asking AI to categorize a new log entry. Which technique is being used?

Answer: A

Explanation:
Few-shot prompting provides several examples of the expected input-output pattern so the AI can apply the same pattern to a new case.


NEW QUESTION # 58
What is a purpose of providing a JSON schema when configuring tool use for an AI agent?

Answer: D

Explanation:
A JSON schema defines the structure, required fields, and data types for tool calls, helping the agent provide valid inputs or outputs that the external tool can process reliably.


NEW QUESTION # 59
Which activity is an indirect prompt injection attack?

Answer: D

Explanation:
An indirect prompt injection occurs when malicious instructions are hidden in external content that the AI reads or retrieves, causing the model to follow attacker-controlled instructions from a poisoned data source.


NEW QUESTION # 60
What is aggregation bias in the context of AI-assisted data analysis?

Answer: C

Explanation:
Aggregation bias occurs when summarized or combined data hides meaningful differences across subgroups, causing important subpatterns to be missed.


NEW QUESTION # 61
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