Microsoft Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
Build and Defend a Baseline Model from a CSV
You receive a CSV during a live interview and are asked to build a useful predictive model. You may use code-completion or agent tools, but you must d...
How do you choose a model?
This question evaluates skills in model selection, task and business-objective definition, data-property analysis, production constraint reasoning, ba...
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 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 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 ...
Explain normalization, regularization, CTR, imbalance handling
This question evaluates mastery of normalization methods, regularization techniques, click-through-rate modeling, and class-imbalance strategies, with...
Clean OCR data and build an LLM dataset
This question evaluates competency in OCR data cleaning and normalization, dataset engineering for LLM fine-tuning, quality filtering, and evaluation ...
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 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...
How would you build and evaluate a classifier?
This question evaluates a data scientist's proficiency in binary classification model evaluation, end-to-end machine learning project design, and mode...
Compare preference alignment methods for LLMs
This question evaluates expertise in preference alignment techniques for large language models—including supervised fine-tuning, RLHF-style reward-mod...
Compare CNN/RNN/LSTM and implement K-means
Deep Learning Concepts and K-means Implementation (Onsite ML Interview) This is a two-part onsite round for a Data Scientist role: a conceptual deep-l...
Explain SHAP and build an ML project
This question evaluates understanding of model explainability using SHAP and the competency to design and operationalize an end-to-end machine learnin...
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\}\...
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
Discuss large language models LLMs: Advances, Product Integration, Production Challenges, and Risk Mitigation Context You are interviewing for a Softw...
Explain Deep Learning to a 5-Year-Old Child
Explain Deep Learning to a 5-Year-Old Child Microsoft Phone-Screen: Machine Learning Fundamentals You are interviewing for a machine learning/data sci...
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...
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, ...