Boston Consulting Group Interview Questions
Practice the exact questions companies are asking right now.
"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."
"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."
Compute posterior and predictive coin probabilities
Bayesian coin: posterior, prediction, and stopping-time expectation Context - You have two coins and will use the same coin for all flips: - Fair co...
Design and sample for credit default prediction
A bank wants a model to predict 90-day credit card default at account-month level for proactive outreach. Class prevalence in production is about 2% d...
Build a leak-free sklearn pipeline
Take-home: Imbalanced Binary Classification Pipeline with scikit-learn You are training a binary classifier on tabular data with the following feature...
Compute averages and binomial/Poisson probabilities
Streaming Mean and Binomial vs Poisson Approximation Part A — Streaming Mean Update You have an existing dataset of N = 1,000 observations with mean 1...
Calculate Probability of Heads and Red Balls Drawing
Quick-Fire Probability (CodeSignal-style) Context: Answer the following independent probability questions, using binomial (independent trials) and hyp...
Derive Probability of Even Sum in Bernoulli Trials
Probability Puzzles: Parity and Runs Context You are given two independent probability problems commonly seen in data-science take-home assessments. A...
Build and evaluate imbalanced binary classifier
Take‑home: Imbalanced Binary Classification with Temporal Split, Calibration, and Operating Point Selection Context You are given an event‑level datas...
Explain AUC, imbalance, losses, and networks
Imbalanced Classification & Regression: ROC/PR, Losses, and Training Strategies You are evaluating a binary classifier and a regression head in a mach...
Reduce overfitting under constraints
Reduce Overfitting Under Latency Constraints (Tabular Regression) Context (assumed) - You have a tabular regression model with a large generalization ...
Defend MSE over MAE for car prices
Choosing MSE vs. MAE for Car Price Regression (Unscaled USD Target) You are training a regression model to predict car prices in USD. The target varia...
Transform and aggregate messy event data
Using pandas (vectorized; no loops), clean, combine, and aggregate the following to produce country/plan day-level metrics for 2025-08-31. DataFrames ...
Identify Causes and Solutions for Fashion Profit Decline
Timed Case: Fashion Retail Profit Decline — Diagnose and Recommend Context You are analyzing a fashion retailer whose profit has declined year-over-ye...
Achieve 0.95 precision via thresholding
Deploying a High-Precision Classifier on an Imbalanced Dataset You are given a binary classification problem with 50,000 samples and ~5% positives. Th...
Scale and Normalize: When to Use Each Method?
Feature Scaling Before Modeling (CodeSignal Notebook) Context You're preparing features in a notebook step before training a model. You have a pandas ...
Evaluate Campaign Lift with Predictive Analytics and Validation Strategy
Evaluate Marketing Campaign Lift (Weekly SKU-Level, 3 Years) Context You have 3 years of panel data at weekly SKU (and optionally region/store) granul...
Calculate Probability and Statistics for Dice Roll Outcomes
Dice Rolls and the Binomial Model Scenario A casino analyst models dice rolls to understand outcome probabilities for marketing promotions. Question Y...
Differentiate Overfitting and Underfitting in Machine Learning
ML/DL Fundamentals for a Recommendation Engine Context You are preparing for a take-home assessment on ML/DL fundamentals relevant to building a recom...
Summarize impact and lessons from your resume
Give a concise 90-second overview tailored to this role. Then deep-dive one project where you changed a business decision using data: state the object...
Explain AUC, activations, ensembles, and imbalance
Machine Learning Metrics and Modeling Choices — Multi-part You are given model scores and binary labels for a small dataset and asked to compute ROC A...
Improve Model Generalization with Cross-Validation and Feature Engineering
Predict Next-Month Orders: Train/Test Split, Pipeline, and AUC Context You are given a cleaned tabular retail dataset as a pandas DataFrame df. The bi...