Drw Machine Learning Engineer Interview Questions
Master your tech interview with our curated database of real questions from top companies.
Train LinearSVC to beat baseline accuracy
Task: Train and Evaluate a LinearSVC to Beat a Baseline Context You are given a binary or multi-class classification dataset split into train and hidd...
Implement portfolio optimization simulation
Question Given price-return time series in a DataFrame, simulate random portfolio weights, compute expected return, volatility, Sharpe ratio, and retu...
Build pipeline for imbalanced classification
Build an Imbalanced Classification Pipeline (scikit-learn + imbalanced-learn) Context You are given a tabular dataset with a severely imbalanced binar...
Explain core ML concepts
ML Theory Check: PCA, Trees, RL, Regularization, Optimization, and Transformers Context: Provide concise, technically correct explanations suitable fo...
Explain core ML and DL fundamentals
ML/DL Concept Questions (Take‑home) Provide concise, correct answers to each prompt. 1) PCA - What do the eigenvectors of the covariance matrix repres...
Implement simulation-based portfolio optimizer in Python
Given a pandas DataFrame 'returns' of daily asset returns (index: dates; columns: tickers) and an annualized risk‑free rate r_f, implement a simulatio...
Build an imbalanced classification pipeline with sklearn
Take-home: End-to-end Imbalanced Binary Classification Pipeline (scikit-learn + imbalanced-learn) Context You are given a tabular, imbalanced binary c...
Process CSV for portfolio returns and metrics
Given one or more CSV files containing daily asset prices or returns and optional portfolio weights, write Python (pandas) code to: a) load, clean, an...
Explain Transformers, activations, and training optimization
Modern Deep Learning: Conceptual Questions (ML Engineer Take-home) You are preparing for a Machine Learning Engineer take-home. Answer the following c...
Train LinearSVC to beat a hidden baseline
Take‑Home: Build a strong LinearSVC pipeline that beats a baseline and generalizes Problem You are given training features X_train and labels y_train ...