find-your-level · diff
v1.0.0 to v1.0.0
101 added, 118 removed. Audit A to A.
---
name: find-your-level
version: 1.0.0
description: >
- Interactive quiz that maps your AI/ML knowledge to a starting point in the
- 260-lesson, 20-phase AI Engineering from Scratch curriculum.
- Trigger phrases: "where should I start", "find my level", "what do I know",
- "which phase", "assess my knowledge", "placement test", "skip ahead"
+ 互动式测验，将你的 AI/ML 知识映射到 AI Engineering from Scratch 课程（20 个阶段、
+ 260+ lesson）的合适起点。触发词："where should I start"、"find my level"、
+ "what do I know"、"which phase"、"assess my knowledge"、"placement test"、
+ "skip ahead"、"我该从哪开始"、"找一下我的水平"、"我会哪些"、"分级测试"、"跳到哪一阶段"
tags: [assessment, onboarding, curriculum, ai-engineering]
---
# Find Your Level
- You are administering a placement quiz for the **AI Engineering from Scratch**
- curriculum (20 phases, 260+ lessons). Your job is to figure out where the
- learner should begin so they skip material they already know and land right
- where the challenge starts.
+ 你正在为 **AI Engineering from Scratch** 课程（20 个阶段，260+ lesson）实施一次
+ 入门分级测验。你的任务是判断学习者应该从哪里开始，让他们跳过已经掌握的内容，
+ 直接落到真正有挑战的位置。
- ## Quiz Structure
+ ## 测验结构
- There are 5 knowledge areas, 2 questions each, 10 questions total. Present
- them in rounds of 2 (one round per area). After the learner answers both
- questions in a round, score that area before moving on.
+ 共有 5 个知识领域，每个领域 2 道题，合计 10 道题。按一轮 2 题进行（每个领域一轮）。
+ 学习者答完一轮的两道题后，先给出该领域得分，再进入下一轮。
- ## Scoring
+ ## 计分
- Each question is worth 1 point (0 = wrong or blank, 1 = correct). Each area
- scores 0-2. Total score ranges from 0 to 10.
+ 每道题 1 分（答错或留空 0 分，答对 1 分）。每个领域得分 0-2 分。总分范围 0 到 10。
- ## Administering the Quiz
+ ## 实施方式
- Start by greeting the learner briefly, then jump straight into Round 1. Use
- **AskUserQuestion** for every question. After each round, tell the learner
- their score for that area (e.g. "Math & Statistics: 2/2") before moving to the
- next round. Keep commentary short. Do not explain the answers until the very
- end.
+ 先简短问候学习者，然后直接进入第 1 轮。每道题都使用 **AskUserQuestion** 来提问。
+ 每轮结束后，告知学习者该领域的得分（例如 "Math & Statistics: 2/2"），再进入下一轮。
+ 讲解尽量简短。在测验全部结束前，不要解释任何答案。
---
### Round 1 -- Math & Statistics
- **Q1.** You have two vectors, a = [1, 2, 3] and b = [4, 5, 6]. What is their
- dot product?
+ **Q1.** 已有两个向量，a = [1, 2, 3]、b = [4, 5, 6]。它们的 dot product 是多少？
- A) 21
- B) 32
- C) 15
- D) 27
- **Correct: B) 32** (1*4 + 2*5 + 3*6 = 32)
+ **正确答案：B) 32**（1*4 + 2*5 + 3*6 = 32）
- **Q2.** A fair coin is flipped 3 times. What is the probability of getting
- exactly 2 heads?
+ **Q2.** 一枚均匀硬币抛 3 次。正好出现 2 次正面的概率是多少？
- A) 1/4
- B) 3/8
- C) 1/2
- D) 1/8
- **Correct: B) 3/8** (C(3,2) * (1/2)^3 = 3/8)
+ **正确答案：B) 3/8**（C(3,2) * (1/2)^3 = 3/8）
---
### Round 2 -- Classical ML
- **Q3.** In a classification task with 90% negative and 10% positive samples,
- a model predicts everything as negative. What is its accuracy?
+ **Q3.** 在一个分类任务中，负样本占 90%、正样本占 10%，模型把所有样本都预测为负，
+ 其 accuracy 是多少？
- A) 50%
- B) 10%
- C) 90%
- D) 0%
- **Correct: C) 90%** (it gets all negatives right, all positives wrong)
+ **正确答案：C) 90%**（所有负样本预测正确，所有正样本预测错误）
- **Q4.** Which of the following is a hyperparameter of a Random Forest?
+ **Q4.** 以下哪一项是 Random Forest 的 hyperparameter？
- - A) The learned split thresholds
- - B) The number of trees
- - C) The leaf node predictions
- - D) The Gini impurity at each node
+ - A) 学习得到的 split thresholds
+ - B) tree 的数量
+ - C) 叶节点的预测值
+ - D) 每个节点的 Gini impurity
- **Correct: B) The number of trees**
+ **正确答案：B) tree 的数量**
---
### Round 3 -- Deep Learning
- **Q5.** During backpropagation, what does the chain rule compute?
+ **Q5.** 在 backpropagation 过程中，chain rule 计算的是什么？
- - A) The optimal learning rate
- - B) The gradient of the loss with respect to each weight
- - C) The number of layers needed
- - D) The batch size
+ - A) 最优 learning rate
+ - B) loss 对每个 weight 的 gradient
+ - C) 所需的 layer 数量
+ - D) batch size
- **Correct: B) The gradient of the loss with respect to each weight**
+ **正确答案：B) loss 对每个 weight 的 gradient**
- **Q6.** What problem do residual connections (skip connections) in ResNet
- primarily address?
+ **Q6.** ResNet 中的 residual connection（skip connection）主要解决了什么问题？
- - A) Overfitting on small datasets
- - B) Vanishing gradients in deep networks
- - C) Slow data loading
- - D) High memory usage
+ - A) 在小数据集上的 overfitting
+ - B) 深层网络中的 vanishing gradient
+ - C) 数据加载缓慢
+ - D) 高内存占用
- **Correct: B) Vanishing gradients in deep networks**
+ **正确答案：B) 深层网络中的 vanishing gradient**
---
### Round 4 -- NLP & Transformers
- **Q7.** In the Transformer architecture, what does the attention mechanism
- compute between?
+ **Q7.** 在 Transformer 架构中，attention 机制计算的是哪些对象之间的关系？
- - A) Pixels and labels
- - B) Queries, Keys, and Values
- - C) Encoder and Decoder only
- - D) Embeddings and positions only
+ - A) 像素和标签
+ - B) Queries、Keys、Values
+ - C) 仅 Encoder 和 Decoder
+ - D) 仅 Embeddings 和 positions
- **Correct: B) Queries, Keys, and Values**
+ **正确答案：B) Queries、Keys、Values**
- **Q8.** What is the main benefit of LoRA (Low-Rank Adaptation) when
- fine-tuning a large language model?
+ **Q8.** 对大型语言模型做 fine-tuning 时，LoRA（Low-Rank Adaptation）的主要优势是什么？
- - A) It trains all parameters from scratch
- - B) It freezes most weights and trains small low-rank update matrices
- - C) It removes the need for any training data
- - D) It doubles the model size for better results
+ - A) 从零开始训练所有参数
+ - B) 冻结大部分权重，只训练小的 low-rank 更新矩阵
+ - C) 完全不需要训练数据
+ - D) 把模型大小翻倍以获得更好效果
- **Correct: B) It freezes most weights and trains small low-rank update matrices**
+ **正确答案：B) 冻结大部分权重，只训练小的 low-rank 更新矩阵**
---
### Round 5 -- Applied AI
- **Q9.** In a RAG (Retrieval-Augmented Generation) system, what happens before
- the LLM generates an answer?
+ **Q9.** 在 RAG（Retrieval-Augmented Generation）系统中，LLM 生成回答之前会发生什么？
- - A) The model is retrained on the query
- - B) Relevant documents are retrieved and injected into the prompt
- - C) The user manually selects context
- - D) The model searches its own weights
+ - A) 用 query 对模型重新训练
+ - B) 检索相关文档并注入到 prompt 中
+ - C) 由用户手动选择上下文
+ - D) 模型在自己的 weights 中进行搜索
- **Correct: B) Relevant documents are retrieved and injected into the prompt**
+ **正确答案：B) 检索相关文档并注入到 prompt 中**
- **Q10.** In a multi-agent system, what is the primary purpose of a
- "coordinator" or "orchestrator" agent?
+ **Q10.** 在多 agent 系统中，"coordinator" 或 "orchestrator" agent 的主要作用是什么？
- - A) To replace all other agents
- - B) To assign tasks, route messages, and manage agent collaboration
- - C) To increase token usage
- - D) To serve as a backup model
+ - A) 取代所有其他 agent
+ - B) 分配任务、路由消息、管理 agent 之间的协作
+ - C) 增加 token 用量
+ - D) 充当备用模型
- **Correct: B) To assign tasks, route messages, and manage agent collaboration**
+ **正确答案：B) 分配任务、路由消息、管理 agent 之间的协作**
---
- ## After All 5 Rounds
+ ## 5 轮全部结束后
- Display the area breakdown and total:
+ 按领域展示明细与总分：
```
Math & Statistics: X/2
Classical ML: X/2
Deep Learning: X/2
NLP & Transformers: X/2
Applied AI: X/2
----------------------------
Total: X/10
```
- ## Score-to-Entry-Point Mapping
+ ## 分数到入门点的映射
- | Total Score | Entry Point | What It Means |
- |-------------|-------------|---------------|
- | 0-3 | Phase 1: Math Foundations | Start from the ground up |
- | 4-5 | Phase 3: Deep Learning Core | You have math and ML basics |
- | 6-7 | Phase 7: Transformers Deep Dive | You know DL, time for transformers |
- | 8-9 | Phase 11: LLM Engineering | Strong foundations, go straight to LLM apps |
- | 10 | Phase 14: Agent Engineering | You know it all, build agents |
+ | 总分 | 入门点 | 含义 |
+ |------|--------|------|
+ | 0-3 | Phase 1: Math Foundations | 从最基础开始 |
+ | 4-5 | Phase 3: Deep Learning Core | 你已有数学和 ML 基础 |
+ | 6-7 | Phase 7: Transformers Deep Dive | 你懂 DL，可以攻 transformers |
+ | 8-9 | Phase 11: LLM Engineering | 基础扎实，直接进入 LLM 应用 |
+ | 10 | Phase 14: Agent Engineering | 全部都会，开始搭建 agent |
- ## Personalized Learning Path
+ ## 个性化学习路径
- After revealing the entry point, generate a markdown table covering all 20
- phases. Use the score to determine the status of each phase. Phases below the
- entry point get "Skip" (the learner already knows the material). Phases at or
- above the entry point get "Do". If a learner scored 1/2 in an area that maps
- to a skippable phase, mark that phase as "Review" instead of "Skip".
+ 公布入门点之后，生成一张覆盖全部 20 个阶段的 markdown 表格。根据得分判断每个阶段的
+ 状态。位于入门点之前的阶段标为 "Skip"（学习者已经会了）。位于入门点及之后的阶段
+ 标为 "Do"。如果学习者在某领域得 1/2 分、而该领域映射到的阶段本来要 Skip，则把那个
+ 阶段改标为 "Review"。
- Area-to-phase mapping for review detection:
- - Math & Statistics (1/2) -> mark Phase 1 as "Review"
- - Classical ML (1/2) -> mark Phase 2 as "Review"
- - Deep Learning (1/2) -> mark Phase 3 as "Review"
- - NLP & Transformers (1/2) -> mark Phases 5 and 7 as "Review"
- - Applied AI (1/2) -> mark Phase 14 as "Review"
+ 复习判定的领域-阶段映射：
+ - Math & Statistics（1/2） -> 把 Phase 1 标为 "Review"
+ - Classical ML（1/2） -> 把 Phase 2 标为 "Review"
+ - Deep Learning（1/2） -> 把 Phase 3 标为 "Review"
+ - NLP & Transformers（1/2） -> 把 Phase 5 和 Phase 7 标为 "Review"
+ - Applied AI（1/2） -> 把 Phase 14 标为 "Review"
- Read the time estimates from ROADMAP.md (the canonical source of truth). Each
- phase heading contains the estimated hours in the format `(~N hours)`. Parse
- these values instead of using hardcoded numbers. This ensures the learning path
- stays in sync with the roadmap as estimates are updated.
+ 从 ROADMAP.md（事实来源的唯一权威）里读取时间估算。每个阶段标题中以
+ `(~N hours)` 的格式标注了预估小时数。解析这些值，而不要使用硬编码数字，确保学习路径
+ 能随 roadmap 更新而保持同步。
- ## Output Format
+ ## 输出格式
- Generate the table like this:
+ 按如下格式生成表格：
```markdown
- | Phase | Name | Status | Est. Hours |
- |-------|------|--------|------------|
+ | Phase | 名称 | 状态 | 预估小时 |
+ |-------|------|------|----------|
| 0 | Setup & Tooling | Skip | -- |
| 1 | Math Foundations | Review | 30 |
| 2 | ML Fundamentals | Skip | -- |
| 3 | Deep Learning Core | Do | 20 |
| ... | ... | ... | ... |
```
- Rules for the table:
- - "Skip" phases show `--` for hours (they do not count toward the total)
- - "Review" phases show full hours (the learner should skim them)
- - "Do" phases show full hours
- - Phase 0 (Setup & Tooling) is always "Skip" regardless of score (it is
- tooling setup, not knowledge)
- - Sum the hours for "Review" and "Do" phases and show the total at the bottom
+ 表格规则：
+ - "Skip" 阶段的小时数显示 `--`（不计入总数）
+ - "Review" 阶段显示完整小时数（学习者应快速过一遍）
+ - "Do" 阶段显示完整小时数
+ - Phase 0（Setup & Tooling）无论得分多少都标为 "Skip"（它属于工具搭建，不是知识）
+ - 把 "Review" 和 "Do" 阶段的小时数加起来，并在末尾给出总计
- After the table, add one sentence with the estimated total: "Your personalized
- path: ~X hours across Y phases."
+ 表格之后，加一句估算总数的话："你的个性化路径：约 X 小时，覆盖 Y 个阶段。"
- Then add a brief recommendation: which phase to start with, and what to focus
- on first based on their weakest area.
+ 接着给出一句简短建议：从哪个阶段开始，以及根据最弱的领域应优先关注什么。