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Cisco 810-110 Exam Syllabus Topics:
| Section |
Weight |
Objectives |
| AI for Data Research and Analysis |
|
- Data exploration and transformation
- 1. Insight generation and validation
- 2. AI-assisted data cleaning
|
| Agentic AI Systems |
|
- Agent architecture
- 1. Multi-step reasoning workflows
- 2. Autonomous agents and orchestration
|
| Ethics and Security |
15% |
- Responsible AI principles
- 1. Bias mitigation
- 2. Fairness, transparency, accountability
- AI security threats
- 1. Misinformation and hallucinations
- 2. AI-specific attack surfaces
- Data privacy and governance
- 1. Secure AI data handling
|
| AI for Code and Workflow Optimization |
|
- AI-assisted software development
- 1. Code generation and debugging
- 2. SDLC integration
- Automation workflows
- 1. Workflow orchestration concepts
- 2. API integration basics
|
| Generative AI Models |
20% |
- Model hosting and architecture
- 1. Cloud vs local deployment trade-offs
- 2. Latency, cost, privacy considerations
- Context and retrieval systems
- 1. RAG, embeddings, vector databases
- 2. Context windows and token limits
- Model fundamentals and use cases
- 1. LLMs and diffusion models overview
- 2. Use cases: summarization, code generation, content creation
|
| Prompt Engineering |
15% |
- Security and robustness
- 1. Mitigation and defensive prompting
- 2. Prompt injection risks
- Prompting techniques
- 1. Few-shot prompting
- 2. Chained and iterative prompting
- Prompt design principles
- 1. Roles, instructions, constraints
|
>> 810-110熱門認證 <<
最新的Cisco 810-110:Cisco AI Technical Practitioner熱門認證 - 權威的VCESoft 最新810-110考題
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最新的 Cisco AI Technical Practitioner (AITECH) 810-110 免費考試真題 (Q62-Q67):
問題 #62
Why is defining a persona considered a best practice for technical prompt engineering?
- A. It reduces the total token usage.
- B. It provides instructions for desired action.
- C. It guides the tone and vocabulary.
- D. It provides a pattern to emulate.
答案:C
解題說明:
Defining a persona guides the AI to respond with the appropriate role-based tone, vocabulary, level of technical depth, and communication style for the intended audience.
問題 #63
Why is a Human-in-the-Loop requirement essential for AI governance in high-stakes environments like healthcare or finance?
- A. to increase the speed of AI response
- B. to protect sensitive customer data
- C. to prevent unauthorized access to data
- D. to ensure the validity of AI decisions
答案:D
解題說明:
Human-in-the-Loop governance ensures that a qualified person reviews, validates, and can override AI-generated decisions when outcomes may significantly affect people, such as medical diagnoses, treatment decisions, credit approvals, or financial risk assessments.
問題 #64
A practitioner must reduce the financial cost of a high-volume summarization task. Which strategy addresses token-based billing?
- A. expanding the total context window
- B. increasing the model temperature
- C. increasing the model sampling rate
- D. minimizing the input prompt size
答案:D
解題說明:
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.
問題 #65
Why are embeddings essential to the retrieval phase of a RAG system?
- A. They reduce model hallucination.
- B. They compress the prompt length.
- C. They represent semantic meaning.
- D. They provide a keyword index.
答案:C
解題說明:
Embeddings convert text into numerical vectors that capture semantic meaning, allowing a RAG system to retrieve content that is conceptually relevant to the user's query rather than relying only on exact keyword matches.
問題 #66
Which software development lifecycle phase utilizes AI to analyze stakeholder documentation for logical inconsistencies and to draft structured user stories?
- A. maintenance
- B. requirements
- C. implementation
- D. testing
答案:B
解題說明:
In the requirements phase, AI can analyze stakeholder documentation to identify logical gaps or inconsistencies and help convert business needs into structured user stories.
問題 #67
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