# Generative AI Certification — Complete Exam Cheat Sheet: 71 Key Terms + Chapter-by-Chapter Study Points
> A last-hours review tool for Taiwan's III Generative AI certification: a full 71-term key-terms reference in five groups, plus the high-frequency points and 'see X, pick Y' distinctions across all four areas — fundamentals, prompting, applied skills, ethics and law. A companion to my prep-method post. Educational sharing, not a question bank.
Published: 2026-07-18
Locale: en
Tags: ai, genai, certification, iiiedu, study, cheatsheet, education
TL;DR: The complete last-hours review tool for the III Generative AI certification. Top half: a full 71-term key-terms reference in five groups (fundamentals/architecture, prompt & agent, tools & applications, data governance & MLOps, ethics & law — Word2Vec, Ground Truth, Data Drift, homomorphic encryption, Red Teaming, LAWS, and more). Bottom half: chapter-by-chapter study points across all four areas (Scaling Laws/Token/Temperature, parameters vs hyperparameters, CLM vs MLM, Data Drift vs Concept Drift, RBAC vs ABAC, TTS/STT/S2S, the PETs privacy family, and so on), each with a 'see X, pick Y' hook. All concept-level, no actual exam questions. Read it alongside my 'how I used AI to prep' method post.
> *Study it widely, question it closely,*
> *reflect on it carefully, discern it clearly, and practice it faithfully.*
> —— The Doctrine of the Mean (pre-Qin classic); translation mine
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This is the **complete cheat sheet** for Taiwan's III Generative AI certification. It isn't a textbook — it's the **fill-the-gaps card for the last hours before the exam**: every term and study point that's easy to miss or easy to confuse, each with a "see X, pick Y" hook.
Concept-level, **no actual exam questions** (those are copyrighted). If you'd rather first read *how* I prepared and passed two certs in two months, here's the companion post: [Two AI Certs in Two Months](/en/blog/genai-cert-exam-prep/).
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# Part 1 — Full Key-Terms Reference (71 terms, five groups)
Each term has a "🎯 how it's used / how it's tested." Items marked [must-know / classic / easily-confused] are the highest-yield — learn to tell them apart.
## Fundamentals & architecture
- **RAG (Retrieval-Augmented Generation)**: retrieve from an external knowledge base first, then generate. 🎯 "how to solve stale knowledge or hallucination" → RAG, no retraining needed.
- **Attention**: lets the model weigh relationships between different positions in the input. 🎯 "why Transformers capture long-range dependencies."
- **Transformer**: Encoder understands input, Decoder generates output. 🎯 GPT = Decoder-only, BERT = Encoder.
- **RLHF (Reinforcement Learning from Human Feedback)**: fine-tune with human feedback to match human preferences. 🎯 why ChatGPT is more obedient/safe than raw GPT.
- **Word2Vec**: maps words to vectors, similar meanings close together. 🎯 static embedding (same word → same vector) ↔ contrast BERT's contextual embedding.
- **Embedding**: turns text/images into vectors. 🎯 RAG / semantic search / recommendation all use it for similarity; it's what a vector DB stores.
- **Tokenizer / Token**: splits text into tokens; a Chinese character ≈ 0.5–1 token. 🎯 API cost and context length are counted in tokens.
- **CNN**: traditional image-recognition architecture. 🎯 trap: "must images use CNN?" → no, Transformers can too and better.
- **GAN**: generator vs discriminator trained adversarially. 🎯 who proposed it → Ian Goodfellow; contrast with diffusion.
- **Diffusion**: generate images by progressively denoising random noise. 🎯 core of SD / Sora; "add noise then learn to denoise," don't confuse with GAN.
- **Pre-training**: learn general knowledge from massive unlabeled data. 🎯 three stages: pre-train (general) → fine-tune (domain/behavior) → RAG (external/live); most compute-heavy = pre-training.
- **Fine-tuning**: change model behavior or inject domain knowledge. 🎯 [most-tested distinction] tone/domain → fine-tuning; changing data → RAG.
- **Overfitting**: great on training, poor on test, no generalization. 🎯 fixes: Dropout, data augmentation, regularization.
- **Temperature**: controls output randomness/creativity. 🎯 raise for brainstorming, lower for precision; =0 always picks the top-probability word.
- **Context Length**: max tokens the model can process at once. 🎯 "forgets the original instruction late in a long chat" = exceeding it.
- **Ground Truth**: the correct answer/label. 🎯 the baseline for judging accuracy.
- **Open Weights vs Open Source**: open weights but restricted license ≠ strictly open source. 🎯 Llama counts as Open Weights.
- **NLU / NLG / NLP**: NLP splits into NLU (understanding) and NLG (generation). 🎯 a chatbot first NLU's your meaning, then NLG's a reply.
- **Quantization**: lower precision (32→8/4-bit) for smaller/faster. 🎯 "less memory, faster, slight accuracy loss" fits big models onto phones.
- **Distillation**: transfer a big model's (Teacher) knowledge to a small one (Student). 🎯 see Teacher/Student → this.
- **Hallucination**: plausible-looking but incorrect/fabricated output. 🎯 "confidently wrong"; the reason for RAG / fact-checking.
- **Zero/One/Few-shot**: give zero / one / several examples. 🎯 if results are poor, upgrade Zero → Few-shot.
- **Chain-of-Thought**: have the model produce intermediate reasoning steps. 🎯 add "think step by step" for math/logic.
- **MoE (Mixture of Experts)**: many experts, only some activated each time. 🎯 "huge params but controllable inference cost" (Grok-1, Mixtral, GPT-4).
## Prompt & agent
- **System vs User Prompt**: System sets the long-term role/rules; User is the immediate instruction. 🎯 Persona/rules → System; everyday questions → User.
- **Prompt Tuning / Soft Prompts**: train only a few soft-prompt vectors, main weights untouched. 🎯 low-cost customization vs full fine-tuning.
- **Agent / AI Agent**: plans autonomously, uses tools, executes multi-step. 🎯 contrast with a chat-only Chatbot.
- **Tool Use / Function Calling**: lets the model call external tools/APIs. 🎯 live prices, math, email; a core Agent capability.
- **LangChain / LlamaIndex**: frameworks for building LLM apps. 🎯 LangChain is general glue; LlamaIndex specializes in private data.
- **ReAct**: reason and act (call tools/search) in alternation. 🎯 the classic "Reasoning + Acting" agent pattern.
- **Multi-agent**: multiple role-based agents collaborating (one writes, one tests, one documents).
- **Jailbreak**: bypass safety filters to produce banned content. 🎯 "breaking safety limits with wordplay" (grandma exploit).
- **Prompt Injection**: hide malicious instructions in the input to hijack the model. 🎯 [easily-confused] Jailbreak (break safety) vs Injection (inject malicious instructions).
- **Self-Consistency**: run inference several times, take the majority vote.
## Tools & applications
- **Text-to-Video**: text directly generates video. 🎯 e.g. Sora; contrast Image-to-Video (needs a still image first).
- **TTS / STT / S2S**: text→speech / speech→text / speech-to-speech translation. 🎯 dubbing TTS, subtitles STT, live cross-lingual call S2S.
- **ControlNet**: adds control conditions to steer image composition. 🎯 OpenPose controls pose, Canny controls edges.
- **Inpainting / Outpainting**: local repaint / extend the canvas outward. 🎯 remove a passerby with Inpainting; extend the frame with Outpainting.
- **Upscaling**: enlarge and fill in detail. 🎯 "enlarge and intelligently add detail," old photos to HD.
- **Style Transfer**: apply one image's style to another's content. 🎯 photo into Van Gogh style; don't confuse with Inpainting/Outpainting.
- **LoRA**: train very few params to learn a specific character/style. 🎯 small file, learns a character/style; a form of PEFT.
- **Voice Cloning / Voice Banking**: clone a voice / bank a voice for those who may lose it. 🎯 medically, ALS patients bank their voice pre-op.
- **Stable Diffusion / Midjourney**: mainstream image generators. 🎯 Midjourney uses --ar for aspect ratio; SD pairs with ControlNet/LoRA.
- **Suno / ElevenLabs**: Suno generates songs; ElevenLabs does voice synthesis/cloning. 🎯 for a full vocal song use Suno.
- **AutoML**: auto-selects models and tunes hyperparameters. 🎯 "lets non-experts build models."
- **Deepfake**: synthesized/face-swapped/voice-cloned fakery. 🎯 ethics must-know: positive (de-aging), negative (fraud); needs detection + transparency labeling.
- **The three chatbot elements**: NLU (understanding), Dialog management, NLG (generation). 🎯 distinguish Intent / Entity / context tracking.
## Data governance & MLOps
- **MLOps / Feature Store**: centrally manage ML features so training/inference stay consistent. 🎯 "avoid mismatch between training and serving features."
- **Data Drift vs Concept Drift** [classic must-know]: input distribution changes vs the input→output relationship/definition changes. 🎯 new words = Data Drift; "the definition of spam itself changed" = Concept Drift.
- **Data Lineage**: trace data from source → transformation → destination. 🎯 trace "where did this number come from" + compliance audit.
- **PII**: personally identifiable information (name, ID, address). 🎯 de-identify or encrypt before processing.
- **Anonymization vs Pseudonymization** [must-know]: anonymization is irreversible vs pseudonymization is reversible. 🎯 fully unrecoverable = anonymization; can map back to the person = pseudonymization.
- **Differential Privacy**: add mathematical noise so individuals can't be recovered. 🎯 "add noise, mathematical guarantee" — publish stats without leaking anyone.
- **Homomorphic Encryption**: compute directly on encrypted data. 🎯 "operate on ciphertext directly."
- **Federated Learning**: train locally, only send parameters back. 🎯 mnemonic "data stays put, the model moves" (phone keyboard prediction, hospital collaboration).
- **SMPC (Secure Multi-Party Computation)**: parties compute jointly without revealing inputs. 🎯 banks computing together without showing each other their data.
- **XAI (Explainable AI)**: make decisions transparent and understandable. 🎯 medical/finance/legal need to explain "why this decision"; global vs local.
- **Red Teaming**: experts play attacker to actively find holes. 🎯 the point is the "active attacker perspective."
- **Data Poisoning**: inject malicious data into training so the model learns badly. 🎯 defense: verify data provenance.
- **Model Inversion Attack**: reconstruct sensitive training data from outputs. 🎯 privacy threat, e.g. reconstruct faces from a face-recognition model.
- **Zero Trust**: never trust, always verify. 🎯 verify every access regardless of source.
## Ethics & law
- **LAWS (Lethal Autonomous Weapons Systems)**: weapons that autonomously select and attack targets. 🎯 the classic military-AI controversy: international bans, humanitarian law, "humans must retain control."
- **Copyright / Fair Use**: whether training on copyrighted works is fair use. 🎯 core of the AI-training infringement dispute; also who owns AI-generated content.
- **GDPR / Machine Unlearning**: right to be forgotten; very hard to precisely delete once in weights. 🎯 "user asks to delete data already trained into the model."
- **EU AI Act**: EU's AI law, risk-tiered into four levels. 🎯 world's first comprehensive AI law: unacceptable / high / limited / minimal.
- **CCPA / PIPL / Taiwan PDPA**: California / China / Taiwan data laws. 🎯 Taiwan = PDPA, needs consent + security measures.
- **Opt-in / Opt-out / Consent**: managing user consent for personal data use. 🎯 "record and manage consent status."
- **Accountability**: being answerable for AI decisions. 🎯 one of the six ethics principles, "trace responsibility when things go wrong."
- **Transparency**: operation and decision logic are open and understandable. 🎯 contrast Accountability (answerability) vs Transparency (openness).
- **Digital Divide**: those lacking network/devices/literacy benefit less from AI. 🎯 extends to "digital colonialism," "cultural homogenization."
- **MHC (Meaningful Human Control)**: humans retain understanding/judgment/intervention. 🎯 stronger than Human-in-the-loop — humans must truly understand and be able to change it.
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# Part 2 — Chapter-by-Chapter Study Points
## Area 1 — Fundamentals
### 1.1 Basic concepts of generative AI
- **Scaling Laws**: more compute/data/parameters → predictably better performance.
- **Token**: a Chinese character is roughly 0.5–1 token, and it's also the billing unit.
- **Temperature** (controls randomness), **Top-P (nucleus sampling)** (sample from the smallest set of words whose cumulative probability reaches P); results vary because of probabilistic sampling.
- **Hallucination**: plausible-looking but incorrect/fabricated output.
- **GAN** (generator vs discriminator, Goodfellow 2014) vs **diffusion models** (add noise to destroy → learn to reverse and denoise).
- Term to know: **vertical AI** = trained and optimized for a specific industry.
### 1.2 Application domains
- Whole-chapter theme: from "automation" to **"augmentation and collaboration"** — boosting professionals, not wholesale replacement.
- Academic norms: AI may assist with polishing/ideation, may not ghost-write, and must be disclosed.
- Human-AI collaboration: fiction = human sets premise/outline, AI expands; translation = translator shifts to **post-editing**.
- **Proof of Concept (PoC)**: test feasibility at small scale before deciding to scale up.
- High-frequency ethics: resume screening can discriminate; therapy AI most fears giving wrong medical advice / mishandling a crisis; policy drafting needs strict human review.
- Content moderation: AI understands context and subtle hate speech better than keyword filters.
- Enterprise RAG: solves keyword search's failure to grasp meaning and synthesize across documents.
- Manufacturing: **generative design** (optimize weight/strength/cost), **predictive maintenance**, quality control **generating rare defect samples**.
- Automated journalism fits structured earnings/sports briefs, not investigative pieces or editorials.
- Term to know: **Procedural Content** = AI generates game art/levels to cut cost.
### 1.3 Technical terminology
- **Parameters** (learned by the model) vs **hyperparameters** (set by humans, e.g. learning rate, batch size).
- **Backpropagation** (compute gradients and update weights during training) vs **Inference** (predict on new data after deployment).
- **Overfitting** (great on training, poor on test — no generalization) vs **Underfitting** (too simple); Bias-Variance Tradeoff (high bias = underfitting, high variance = overfitting).
- **Precision vs Recall** trade off; **F1** is their **harmonic mean** (trap: not the arithmetic mean).
- Decoding: Greedy (repetitive), **Beam Search (width=1 equals Greedy)**, Top-K, Temperature=0 most deterministic.
- Architecture: Encoder-Decoder → Seq2Seq (translation); Decoder-only (GPT continuation); Self-Attention (words within a sentence); Cross-Attention (across two sequences).
- **RNN** hard to parallelize + long-range forgetting; **LSTM** gating solves vanishing gradients; **GRU** is a simplified LSTM.
- **Word2Vec** (static, same word same vector) vs **BERT** (contextual, same word changes with context, like "bank").
- Pretraining tasks: GPT = **CLM** (causal/unidirectional); BERT = **MLM** (masked/bidirectional).
- Terms: Ground Truth, Dropout (randomly disable neurons to fight overfitting), Data Augmentation (rotate/crop/add noise), Quantization (32→8/4-bit, smaller and faster), LLM-as-a-Judge, Ablation Study (remove a component to see its effect).
### 1.4 Technical principles
- **SD ecosystem**: Automatic1111 (most complete WebUI), ComfyUI (node-based), Fooocus (minimal, high quality), Civitai (model/LoRA community).
- Parameters: **CFG Scale** (how closely the image follows the prompt), Sampler (detail/convergence), **Checkpoint** (full model, sets style), **LoRA** (small file, learns a specific character/style).
- ControlNet preprocessors: Canny (edges), OpenPose (pose).
- RAG tools: LangChain (glue framework), LlamaIndex (connect private data to an LLM).
- Local LLM: Ollama, LM Studio, **GGUF (a "format," not software)**, GPT4All.
- Search-type: **Perplexity.ai** (cites sources, fights hallucination), Poe (aggregates many models).
- Inference optimization: TensorRT (NVIDIA acceleration), ONNX (cross-framework format), QAT (quantize during training) vs PTQ (quantize after training).
- Edge: Edge AI is **not** "unlimited compute"; NPU for low-power inference; TOPS measures AI chip performance.
- Terms: **MoE** (Mixture of Experts, only part activated each time), IP-Adapter (use an image as a prompt), Delimiter (separates instructions from data, blocks injection), Pipeline (Hugging Face wraps pre/inference/post into a one-line API).
## Area 2 — Prompting
### 2.1 Prompt optimization
- A good prompt states: goal / audience / tone / length and other concrete constraints.
- Reasoning guidance: **CoT** (think step by step), Least-to-Most (break into sub-questions), Step-Back (abstract first), Tree of Thoughts (multi-path + backtrack), Self-Consistency (majority vote).
- Long-conversation failures: **Prompt Decay** (forgets the original instruction), Instruction Creep (instructions pile up messily), **Lost in the Middle** (ignores the middle).
- Security: **Prompt Injection (broad) ⊃ Jailbreak (a subclass)**; Prompt Leaking (trick out the System Prompt); defenses = Sandwich Defense, Instruction Defense.
- Delimiters (```, ###, XML) separate instructions from data to block injection; Claude is good at XML.
- Temperature high = creative; Top-P low = conservative/deterministic.
- Best way to reduce hallucination: require "answer only from the provided data; say you don't know if you don't."
- Image parameters: --stylize (artistic), --chaos (variety), :: (weighting), Seed (lock composition), Negative Prompt (exclude elements).
- Bias: English prompts tend to be higher quality; without a specified culture, output skews Western (Wedding defaults to a white dress).
- Terms: Meta-Prompting (ask AI to write your prompt), Grandma Exploit, Flipped Interaction (the model asks you questions instead).
### 2.2 Data governance
- **Data Drift** (input distribution changes) vs **Concept Drift** (input-output relationship changes) [classic mix-up].
- Access control: **RBAC** (role-based) vs **ABAC** (attribute-based, finer-grained).
- Data architecture: Data Lake (unstructured) / Warehouse (ACID) / Lakehouse (combined) / Fabric (unified access) / Mesh (decentralized, data as a product).
- GDPR right-to-be-forgotten technical challenge = **Machine Unlearning** (very hard to precisely delete once trained into weights).
- Terms: Golden Record (the single cleaned, correct version / SSOT), Dark Data (collected but never analyzed), Data Poisoning, Shadow AI (employees using unapproved AI tools), HNSW (approximate nearest-neighbor vector algorithm).
### 2.3 Recent developments
- Linear-complexity architectures (solving Transformer's quadratic cost): Mamba (SSM), RWKV, RetNet, Griffin, Jamba (hybrid).
- Inference acceleration: Flash Attention (IO-aware), PagedAttention (KV cache paging, vLLM), Speculative Decoding (small model drafts, large model verifies).
- Quantization extreme: BitNet b1.58 (weights of -1/0/1, ≈1.58 bit).
- RAG variants: Graph RAG (knowledge graph), Self-RAG (self-reflective), CRAG (assesses relevance, triggers web search), Agentic RAG.
- MoE: Grok-1 (314B but ~25% used per pass), DBRX, Mixtral; large total params, controllable inference cost.
- Post-training (RLHF/RLAIF) decides safety/alignment, key to reasoning models like o1.
- AI Agent vs Chatbot: Agent has planning/tools/loops; e.g. Devin, SWE-agent.
- Science breakthroughs: AlphaFold 3 (protein/DNA/RNA), GraphCast (weather), GNoME (new crystals), AlphaGeometry (geometry proofs).
- Natively multimodal (Gemini 1.5, end-to-end) beats stitching models together.
- Terms: Compound AI Systems (multiple models/tools combined), Model Merging (merge fine-tuned weights), Sovereign AI (nations build their own AI infrastructure), SynthID (invisible watermark), Embodied AI (AI in physical robots).
### 2.4 Self-directed learning
- Methods: **Feynman technique** (explain it to a novice to test real understanding), Project-Based, Building in Public, Peer Learning.
- Dev tools: Cursor (AI editor that understands the whole project), VS Code+Copilot, Replit (in-browser), Colab (free GPU).
- Cloud certs: Google PMLE / Azure AI Engineer / AWS ML-Specialty.
- **Data preprocessing often takes 80%+ of an AI project's time.**
- Math mapping: linear algebra → matrices/tensors; calculus → gradient descent/backprop; probability/statistics → predicting the next token.
- **First step of self-learning = get hands-on** (sign up for ChatGPT/Midjourney and try it yourself).
- Terms: roadmap.sh, Pomodoro (25+5), Growth Mindset vs Abundance Mindset, AutoGPT / BabyAGI.
## Area 3 — Applied skills (eight generation tools)
### 3.1 Text generation
- Core positioning = **"intelligent copilot"**: boost efficiency, replace repetitive low-value writing, not human creation.
- Task names (loves testing "pick the right function"): Summarization / Paraphrasing / Expansion / Translation / Sentiment Analysis.
- Customer-service rule: AI tries first, then hands off seamlessly to a human (with a summary) for professional/medical/legal/sensitive matters.
- Professional docs (legal/medical/financial): AI is only a draft/aid, human review required.
- Consistency control: Glossary/Style Guide, brand tone-of-voice guide, persona prompt.
- Fairness: job descriptions avoid gender/age/race bias; news uses factual statements.
- Multimodal bridging: text AI often acts as the "input layer" (video scripts, image prompts, narration) but doesn't make images/audio itself.
- Personalized recommendations: not just right/wrong, but explanations + avoiding filter bubbles.
- Tools: Grammarly (grammar + tone), Persona-based Content, Style Guide / Glossary.
### 3.2 Image generation
- Three editing techniques (loves the distinction): **Inpainting** (remove/repair local), **Outpainting** (extend the canvas), **Upscaling** (enlarge and fill detail).
- ControlNet/OpenPose for precise composition/pose control.
- Career impact = replaces some repetitive work, still needs human creative direction/taste/finishing.
- E-commerce: composite product photos into scene backgrounds / virtual models.
- Copyright & ethics: complex ownership, bias from training data, must label AI-generated and be traceable.
- Ethics line: no porn/violence/hate, no non-consensual Deepfake.
- Prompt keywords "photorealistic"/"8k" = more realistic detail (not size change / speed-up).
- Tools: Stable Diffusion, Virtual Staging, virtual try-on/makeup, Remini (photo restoration).
### 3.3 Presentation generation
- Core principle **"Less is More"**: minimal text, more visuals.
- Script/outline needs: topic, target audience, talk length, core arguments.
- **Speaker Notes** = each slide's expanded explanation / data source / speaking prompts.
- Three stages: before (content review) / during (delivery analysis) / after (summary report).
- Storytelling: Problem-Solution, STAR frameworks.
- Visual consistency, distinguish: visual coherence / language style / brand norms.
- Fact-checking: needs reliable data + human review, not just AI auto-correction.
- **Alt Text** = auto-generated image description for screen readers = accessibility.
- Data functions in three layers: live updates / data sync (Excel/Sheets) / data governance.
- Interactivity: Q&A hosting, polls, quizzes, gamification (points/leaderboard/badges).
- Virtual Presenter, version control (revert to any version), chart selection (recommends by data type + goal).
### 3.4 Audio generation
- **The three voice conversions** (loves testing): **TTS** (text→speech), **STT** (speech→text), **S2S** (speech-to-speech translation, converts directly to speech).
- TTS naturalness = simulating pitch/intonation/stress/rhythm.
- Vocaloid-type = turning text into a "singing voice" of a specific timbre.
- Music generation: rhythm / harmony / arrangement / composition.
- Dubbing / ADR (automated dialogue replacement) = synthesized speech matching lip-sync/emotion.
- Audio editing: Adobe Podcast (Enhance Speech) removes noise/echo, lifts to studio quality.
- **Three security items** (easily confused): voice biometrics (identity verification) / AI voice forensics (detect if AI-generated/tampered) / audio watermark (provenance).
- AI voice fraud = cloning a relative's voice to scam (the most concrete ethical risk).
- Two-way emotion: voice emotion recognition vs emotion expression generation — don't mix.
- Foley Sound / 3D spatial audio.
### 3.5 Video generation
- Covers the full pre- to post-production pipeline.
- **Text-to-Video vs Image-to-Video** (the latter needs one still image + an action prompt).
- Facial animation (from voice/emotion/mocap), voice cloning, De-aging, visual style transfer.
- Generative Fill (remove objects/extend background), video background removal, Upscaling.
- **Deepfake = the biggest ethical controversy** → needs detection + transparency labeling.
- Ethics: no porn/violence/hate/non-consensual Deepfake.
- For professionals = faster and cheaper, still needs human creative direction; promotes the "democratization of filmmaking."
- Tools: Pika Labs (Motion Brush), HeyGen (Lip-syncing).
### 3.6 Code generation & websites
- AI Pair Programming (a virtual programmer collaborating in real time).
- Code Refactoring (optimize structure/readability without changing external behavior).
- Auto-generate unit tests; natural language to SQL (lets non-technical users query databases).
- IaC (Terraform/CloudFormation, manage infrastructure as code), containerization (Dockerfile/K8s), CI/CD (GitHub Actions).
- Web integration: payments (Stripe/PayPal), CRM (Salesforce), ERP (SAP).
### 3.7 Data analysis
- Core = auto-detect patterns/anomalies/trends.
- AutoML (auto modeling, lower barrier), BI (natural-language charts/reports, NL-to-SQL).
- Feature engineering, data cleaning, anomaly detection (fraud/failure).
- Customer churn prediction vs customer lifetime value (LTV) prediction.
- Data visualization (auto chart choice) / data storytelling, predictive maintenance, supply-chain optimization.
### 3.8 Chatbots
- **Intent Recognition** ("book a flight") / **Entity Extraction** ("tomorrow," "Taipei").
- Rule-based (needs all paths defined manually, inflexible) vs AI-powered.
- Proactive Assistance, Emotional Intelligence (empathetic responses).
- Customer service: multi-channel, self-service, seamless handoff with summary, multilingual.
- Crisis handling: detect suicidal ideation → immediately provide professional resources and referral.
## Area 4 — Ethics & law
### 4.1 Social and individual impact
- Whole-chapter theme "**double-edged sword**": every domain brings efficiency and new risk at once.
- Augmentation vs substitution.
- **Automation Bias**: over-trusting AI, blindly following even when it's wrong.
- Deepfakes / disinformation threaten elections, news credibility, social trust.
- Echo Chamber / Filter Bubble → social polarization.
- Human-AI emotional attachment, cognitive decline.
- Digital divide, three variants: digital divide (lack of devices/literacy) / digital colonialism (loss of data sovereignty) / cultural homogenization (marginalizing minorities).
- Labor: Upskilling/Reskilling, **UBI (universal basic income)**, **Ghost Work** (the low-paid labeling/moderation labor behind AI).
- Environmental cost: training/inference energy use → higher carbon emissions (a problem, not a green benefit).
### 4.2 Ethical principles
- **Non-maleficence (do no harm, passive) vs Beneficence (do good, active)**.
- Human control gradient: Human-in-the-loop → **Meaningful Human Control (MHC)** (retain understanding/judgment/intervention).
- XAI two layers: global explainability (whole model) vs local explainability (single prediction).
- **EU AI Act**: the world's first comprehensive AI law, risk-tiered.
- **NIST AI RMF**: voluntary, four stages — govern/map/measure/manage.
- Bias: detection (diversity analysis + fairness testing + counterfactual) vs mitigation (pre-processing + training constraints + post-processing).
- Alignment, Double-use Problem, Red Teaming.
- Terms: MHC, global vs local explainability, regulatory sandbox (evaluate ethical impact in a controlled environment).
### 4.3 Laws and regulations
- US **AI Bill of Rights**: non-binding guiding principles (not law).
- Liability: when AI errs, it's **not "the AI is responsible"** — it falls on designer/manufacturer/deployer/user.
- Copyright: disputes over ownership of AI-generated content; whether training on copyrighted works infringes; whether AI can be an inventor.
- Data sovereignty / localization: data must be stored in a specific geography.
- Military: LAWS international bans, humanitarian law, chain-of-command responsibility.
- Digital ethics codes have no legal force (a code ≠ a law); AI legal personhood is still debated, unresolved.
- Terms: AI Bill of Rights, PIPL (China's data law) / Taiwan's data law, Data Localization, LAWS.
### 4.4 Privacy and data security
- **Privacy-Enhancing Technologies (PETs) family [the most-tested distinction of the chapter]**:
- Differential privacy (add mathematical noise, can't identify an individual)
- Homomorphic encryption (compute directly on encrypted data)
- Federated learning (train locally, only send parameters — data stays put, the model moves)
- Secure Multi-Party Computation (SMPC) (parties compute jointly without leaking inputs)
- Data masking (replace with correctly-formatted fake data for test environments)
- Access control: RBAC (role) / ABAC (attribute, finer) / ACL (list) / least privilege.
- **Zero Trust architecture**: "never trust, always verify."
- Encryption's three states: at Rest / in Transit / KMS key management.
- Threats: Data Poisoning, Model Inversion Attack (reconstruct training data such as faces), Adversarial Robustness (resisting adversarial samples, a defensive goal).
- AI security applications: SIEM (unified monitoring), IDS (intrusion detection), SOC (security operations center).
- Isolation: **TEE (Trusted Execution Environment, hardware isolation)**, Secure Data Enclaves; MFA, separation of duties.
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**How to use it**: the day before, run through "Part 1 — terms" and the distinction pairs (Data Drift vs Concept Drift, TTS/STT/S2S, Precision vs Recall, Non-maleficence vs Beneficence, the PETs family) twice; skim the rest for recognition. Good luck out there — for the method, see the companion post: [Two AI Certs in Two Months](/en/blog/genai-cert-exam-prep/).