Mid-level interview questionAsked at Adobe · interview write-up
Explain data leakage and how you'd handle missing data, then compare MSE, MAE and log loss across linear and logistic regression.
Model answer
Leakage is training on information unavailable at prediction time: future data, target-derived features, or scalers fitted before splitting. Split first.
For missing data, ask why it's missing, then impute from training statistics and add a missing indicator.
MSE fits the conditional mean and punishes outliers; MAE fits the median. Logistic regression uses log loss: the Bernoulli likelihood, convex in the weights. MSE on a sigmoid is non-convex and its gradients vanish when saturated.
Practise the rest
35 Training questions, 492 in all, each with a model answer. Free.
More from Adobe interview questions.
More Training interview questions
- Implement masked cross-entropy in NumPy that stays numerically stable, with label smoothing and a temperature.
- Build a pipeline that finds math documents in web crawls and scores their quality.
- Explain KL divergence for language models: which direction is which, what happens when supports don't match, and how it's used as a regulariser.
- When would you use mean squared error versus cross-entropy? And what is LoRA?
- Explain Adam and its bias correction, and why AdamW decouples weight decay.
- Explain policy types in RL and modern policy-gradient methods such as TRPO, PPO and GAE, then compare linear attention and grouped-query attention.