Microsoft Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
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Explain KNN and how to tune it
K-Nearest Neighbors (KNN) fundamentals You are interviewing for a Data Scientist role. 1. Explain how the KNN algorithm works for both classification ...
How do you choose a model?
You are building machine learning features for a spreadsheet assistant. Explain how you would choose an appropriate model for a new problem. In your a...
Clean OCR data and build an LLM dataset
Problem: OCR data practice (cleaning → LLM-ready data) You are given an OCR dataset intended to train or fine-tune an LLM to improve OCR text quality....
Compute and plot a precision–recall curve
Precision–Recall (PR) curve coding / evaluation You are given a binary classifier’s outputs on a dataset: - y_true: array of true labels in \(\{0,1\}\...
Explain SHAP in an ML System
Describe how you would build an end-to-end machine learning system for a business use case such as churn prediction, ad conversion prediction, or cont...
Explain normalization, regularization, CTR, imbalance handling
You are interviewing for an applied ML role. Answer the following fundamentals clearly and concretely (you may use equations and practical examples): ...
Compare preference alignment methods for LLMs
Question You’re asked to discuss preference alignment approaches for large language models. Task Compare several alignment methods and explain when yo...
Explain metrics, regularization, and ablation studies
You are interviewing for an Applied Scientist role. 1) For a binary classification problem, explain the following and when you would use each: - Preci...
Explain bias-variance and evaluate a classifier
You are interviewing for an Applied Scientist internship. Answer the following ML foundations questions. 1) Bias–variance - Define bias and variance i...
Explain SHAP and build an ML project
Part A: SHAP 1. What is SHAP (SHapley Additive exPlanations) trying to measure? 2. How do you interpret: - A local SHAP explanation for a single pr...
Implement robust k-means from scratch
Implement K-Means Clustering From Scratch (Production-Ready) Context You are asked to implement K-Means clustering from scratch for a machine learning...
Compare CNN, RNN, and LSTM rigorously
Sequence Modeling: Rigorous Comparison of CNNs, RNNs, and LSTMs Context and assumptions: - We are modeling 1D sequences of shape (batch=32, time=100, ...
Compare CNN/RNN/LSTM and implement K-means
Deep Learning Concepts and K-means Implementation (Onsite ML Interview) Part A: CNNs vs RNNs and LSTMs Contrast CNNs and RNNs for the following modali...
Design a model for imbalanced conversions
Predicting Purchase Propensity After a Campaign (5% Positives) You previously ran a marketing campaign to 10,000 customers and observed 500 purchases ...
Discuss large language models
LLMs: Advances, Product Integration, Production Challenges, and Risk Mitigation Context You are interviewing for a Software Engineer role focused on m...
Cluster city name variants into canonical entities
Normalize City Names for Vote Aggregation Context You have voting records containing a free-text city field. The same city may appear in many forms (e...
Explain KNN and PCA and key tradeoffs
In a Data Scientist internship interview, you are asked ML fundamentals: 1) K-Nearest Neighbors (KNN) - Explain how KNN works for classification and r...
Explain Deep Learning to a 5-Year-Old Child
Microsoft Phone-Screen: Machine Learning Fundamentals You are interviewing for a machine learning/data science role and should provide concise, struct...
How would you build and evaluate a classifier?
You are building a binary classification model for a business use case such as fraud detection, churn prediction, lead scoring, or content moderation....