## Scenario An interviewer is deep-diving into an ML project you built (you can assume it is a supervised model unless specified otherwise). They want you to justify model choices, evaluation, and training decisions. ## Part A — Evaluation design and metrics 1. Describe **how you evaluate** your model end-to-end (data split strategy, validation protocol, test usage). 2. Which **metrics** do you use and **why** (business/ML tradeoffs)? 3. Provide the **mathematical definitions** for the metrics you mention (e.g., accuracy, precision/recall, F1, ROC-AUC, PR-AUC, log loss, MSE/MAE, calibration metrics). 4. Propose a **better evaluation workflow** than a single holdout set (e.g., cross-validation, time-based split, stratification, repeated runs, confidence intervals). 5. If you can add **human labels** (or human evaluation), explain: - what you would label, - how you would ensure quality (guidelines, inter-annotator agreement), - how it improves the evaluation signal. 6. If you have **no labels**, what is the **simplest** way to estimate whether two model outputs/answers are **similar**? ## Part B — Transformers vs RNNs on long inputs 1. Compare **Transformer** and **RNN/LSTM/GRU** architectures. 2. For **very long sequences**, discuss the pros/cons of each (training stability, ability to capture long-range dependencies, compute/memory). 3. Explain why **attention** can capture long-range dependencies, and why vanilla RNNs often struggle. ## Part C — Detecting distribution mismatch in images You have two sets of images (Set A and Set B). How would you test whether they appear to come from the **same underlying distribution**? ## Part D — Optimizers Compare the practical differences and tradeoffs among **SGD (with/without momentum)**, **RMSProp**, **Adam**, and **AdamW**. When would AdamW be preferable?