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| Section | Weight | Objectives |
|---|
| Generative AI Models | 20% | - Model hosting and architecture
- 1. Cloud vs local deployment trade-offs
- 2. Latency, cost, privacy considerations
- Model fundamentals and use cases
- 1. LLMs and diffusion models overview
- 2. Use cases: summarization, code generation, content creation
- Context and retrieval systems
- 1. Context windows and token limits
- 2. RAG, embeddings, vector databases
|
| AI for Data Research and Analysis | | - Data exploration and transformation
- 1. AI-assisted data cleaning
- 2. Insight generation and validation
|
| AI for Code and Workflow Optimization | | - Automation workflows
- 1. Workflow orchestration concepts
- 2. API integration basics
- AI-assisted software development
- 1. SDLC integration
- 2. Code generation and debugging
|
| Ethics and Security | 15% | - Responsible AI principles
- 1. Fairness, transparency, accountability
- 2. Bias mitigation
- AI security threats
- 1. AI-specific attack surfaces
- 2. Misinformation and hallucinations
- Data privacy and governance
- 1. Secure AI data handling
|
| Prompt Engineering | 15% | - Prompt design principles
- 1. Roles, instructions, constraints
- Prompting techniques
- 1. Few-shot prompting
- 2. Chained and iterative prompting
- Security and robustness
- 1. Prompt injection risks
- 2. Mitigation and defensive prompting
|
| Agentic AI Systems | | - Agent architecture
- 1. Autonomous agents and orchestration
- 2. Multi-step reasoning workflows
|
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Cisco AI Technical Practitioner Sample Questions (Q21-Q26):
NEW QUESTION # 21
Which algorithm category is used when no labels exist in data?
- A. Deterministic routing
- B. Reinforcement learning only
- C. Unsupervised learning
- D. Supervised learning
Answer: C
Explanation:
Unsupervised learning discovers patterns or groupings without labeled outputs.
NEW QUESTION # 22
A company discloses to its users that their support tickets are being summarized by an AI model.
Which responsible AI principle does this action support?
- A. transparency
- B. bias mitigation
- C. system safety
- D. inclusiveness
Answer: A
Explanation:
Transparency means clearly informing users when AI is being used, what it is doing, and how it may affect their data or experience.
NEW QUESTION # 23
In supervised machine learning, what is required?
- A. No data
- B. Only encrypted traffic
- C. Labeled datasets
- D. Random hardware failures
Answer: C
Explanation:
Supervised learning trains models using labeled input-output pairs.
NEW QUESTION # 24
Which activity occurs when a developer uses AI to generate a complete Python class for a new API client based on a high-level description?
- A. providing inline co-pilot assistance
- B. performing code scaffolding
- C. implementing predictive function logic
- D. executing legacy code refactoring
Answer: B
Explanation:
Code scaffolding uses AI to generate the initial structure and boilerplate for a new component, such as a complete Python class for an API client based on a high-level description.
NEW QUESTION # 25
Which workflow mechanism is being used when a company implements a policy where an AI agent can draft a customer email, but a human must authorize the transmission?
- A. drift monitoring
- B. approval gate
- C. data lineage
- D. injection mitigation
Answer: B
Explanation:
An approval gate requires human authorization before a high-impact or externally visible action is executed, such as sending an AI-drafted customer email.
NEW QUESTION # 26
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