Find Your Level

Q1. You have two vectors, a = [1, 2, 3] and b = [4, 5, 6]. What is their dot product?

• A) 21
• B) 32
• C) 15
• D) 27

Correct: B) 32 (14 + 25 + 3*6 = 32)

Q2. A fair coin is flipped 3 times. What is the probability of getting exactly 2 heads?

• A) 1/4
• B) 3/8
• C) 1/2
• D) 1/8

Correct: 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?

• A) 50%
• B) 10%
• C) 90%
• D) 0%

Correct: C) 90% (it gets all negatives right, all positives wrong)

Q4. Which of the following is a hyperparameter of a Random Forest?

• A) The learned split thresholds
• B) The number of trees
• C) The leaf node predictions
• D) The Gini impurity at each node

Correct: B) The number of trees

Round 3 -- Deep Learning

Q5. During backpropagation, what does the chain rule compute?

• 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

Correct: B) The gradient of the loss with respect to each weight

Q6. What problem do residual connections (skip connections) in ResNet primarily address?

• A) Overfitting on small datasets
• B) Vanishing gradients in deep networks
• C) Slow data loading
• D) High memory usage

Correct: B) Vanishing gradients in deep networks

Round 4 -- NLP & Transformers

Q7. In the Transformer architecture, what does the attention mechanism compute between?

• A) Pixels and labels
• B) Queries, Keys, and Values
• C) Encoder and Decoder only
• D) Embeddings and positions only

Correct: B) Queries, Keys, and Values

Q8. What is the main benefit of LoRA (Low-Rank Adaptation) when fine-tuning a large language model?

• 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

Correct: B) It freezes most weights and trains small low-rank update matrices

Round 5 -- Applied AI

Q9. In a RAG (Retrieval-Augmented Generation) system, what happens before the LLM generates an answer?

• 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

Correct: B) Relevant documents are retrieved and injected into the prompt

Q10. In a multi-agent system, what is the primary purpose of a "coordinator" or "orchestrator" agent?

• 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

Correct: B) To assign tasks, route messages, and manage agent collaboration

After All 5 Rounds

Display the area breakdown and total:

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

Personalized Learning Path

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".

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"

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.

Output Format

Generate the table like this:

| Phase | Name | Status | Est. Hours |
|-------|------|--------|------------|
| 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

After the table, add one sentence with the estimated total: "Your personalized