Data Science Interview Guide 2026 | 209 Real Q&A | StepUpDataLab
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2026 EDITION/DATA SCIENCE INTERVIEW PREP

The Complete Data Science Interview Guide

209 real interview questions with in-depth answers, 2–3 follow-up questions each, and interviewer insight on what is actually being assessed. 234 pages across 14 sections, from SQL and Python to GenAI and ML system design.

209
QUESTIONS
234
PAGES
14
SECTIONS
50+
COMPANIES

Questions compiled from candidate-reported interview experiences on Glassdoor, InterviewQuery, PracHub, DataLemur, Exponent, AmbitionBox and Prepfully. Company names indicate where questions were reported — not sponsorship or affiliation.

S T E P U P D A T A L A B
From Rookie to Pro
THE COMPLETE
DATA SCIENCE
INTERVIEW GUIDE
2026 EDITION
209 Real Interview Questions
In-Depth Answers · Follow-Ups
ML · DL · AI/GenAI · SQL · Python
Statistics · A/B Testing · System Design
FRESHERS INTERNS EXPERIENCED
233+ PAGES · PDF
WHERE INTERVIEWS ARE ACTUALLY DECIDED
BASE QUESTION
What is the bias-variance tradeoff?
FOLLOW-UP
How would you fix a model that has high bias?
FOLLOW-UP
Low training error but high test error — what is happening, and what do you do?
FOLLOW-UP
Can you have low bias and low variance simultaneously?
Every one of the 209 questions carries 2–3 answered follow-ups and an interviewer-insight note. See how a question is built →
HOW ONE QUESTION WORKS

You are not preparing for the question. You are preparing for what comes after it.

Most interview PDFs stop at the answer. Real interviews do not. Every question in this guide is built as a five-layer unit, so you rehearse the probing that actually decides the round.

01
Real interview question
Tagged Fresher, Mid-Level or Senior, with the companies where it was reported.
02
In-depth answer
The concept, the trade-offs, and how you would diagnose it on a real model.
03
Follow-up questions
2–3 realistic probes per question, each with its own worked answer.
04
Interviewer insight
71 notes explaining what the interviewer is really evaluating.
05
Practical context
The production or business framing that turns a definition into judgement.
SECTION 2 · MACHINE LEARNING · Q1 SAMPLE PAGE
FRESHER Asked at: Amazon, Google, Meta, TCS, Infosys, Flipkart, Deloitte
Q1. What is the bias-variance tradeoff?
ANSWER

Bias is the error from overly simplistic assumptions in a model — it causes underfitting. Variance is the error from a model being too sensitive to small fluctuations in the training data — it causes overfitting. Reducing bias typically increases variance, and vice versa.

Practically, I diagnose this by plotting learning curves: if training error is low but validation error is high, the model has high variance…

FOLLOW-UP QUESTIONS — WHAT THE INTERVIEWER ASKS NEXT
▶How would you fix a model that has high bias?
▶Your model shows low training error but high test error. What is happening and what do you do?
▶Can you have low bias and low variance simultaneously?
INTERVIEWER INSIGHT

“Interviewers are not looking for a textbook definition. They want you to explain how you would diagnose whether a real model is suffering from high bias or high variance, and what specific actions you would take.”

EXCERPT SHOWN IN FULL · 208 MORE QUESTIONS IN THE GUIDE
WHAT'S INSIDE

Fourteen sections, ordered the way you should actually prepare

STAGE 01 · FOUNDATIONS
1 · Introduction
How to use the guide, 2-week / 30-day / 60-day tracks, and the 2026 interview process round by round.
5 · SQL 31 Q&A
Joins, GROUP BY, window functions, CTEs, self-joins, optimisation and real-world query patterns.
6 · Python 30 Q&A
Core Python, Pandas, NumPy, scikit-learn and applied coding challenges.
STAGE 02 · CORE DATA SCIENCE
7 · Statistics & Probability 25 Q&A
Distributions, CLT, p-values, confidence intervals, hypothesis testing, Bayesian thinking.
2 · Machine Learning 35 Q&A
Fundamentals, core algorithms, feature engineering, ensembles, production ML.
8 · A/B Testing & Experimentation 10 Q&A
Test design, sample size and power, peeking, CUPED, metric selection, novelty effects.
STAGE 03 · ADVANCED AI
3 · Deep Learning 30 Q&A
Neural network fundamentals, CNNs, RNNs, LSTMs, Transformers, attention, transfer learning.
4 · AI / GenAI & LLMs 30 Q&A
LLM fundamentals, tokenisation, prompt engineering, RAG, fine-tuning and LoRA, agents.
9 · ML System Design 8 Q&A
Fraud detection, recommendations, ETA prediction, search ranking, moderation, churn.
STAGE 04 · INTERVIEW APPLICATION
10 · Behavioral / HR (STAR) 10 Q&A
Project walkthroughs, conflict, ownership, prioritisation, explaining models to stakeholders.
11 · Real-Life Case Studies 8 + take-home
5 SQL and 3 Python case studies with full code, business interpretation and a 3–5 hour take-home walkthrough.
12 · Last-Minute Revision
One-line rapid-fire answers by topic and by company archetype for the night before.
STAGE 05 · CAREER PREPARATION
13 · Resume Tips & Templates
ATS rules, section order by experience level, bullet formulas, and 6 templates from Fresher to Research-Leaning.
14 · Appendix
Formula cheat sheet (metrics, statistics, ML), a glossary of key terms, and a curated resource list.
Sections 1, 12, 13 and 14 are included with the Complete Guide.
INSIDE THE BOOK

Four excerpts, straight from the finished guide

SECTION 1 · THE 30-DAY PREP PLAN

Assumes 2–3 focused hours a day. Four weeks, day by day.

WEEK 1SQL fundamentals → advanced SQL → core Python → Pandas & NumPy
WEEK 2Statistics → ML fundamentals → ML algorithms → A/B testing
WEEK 3Deep learning → GenAI & LLMs → ML system design → case studies
WEEK 4Behavioral (STAR) → resume & LinkedIn → mock interviews → weak areas → last-minute revision
SECTION 12 · LAST-MINUTE REVISION

One-line answers to scan 30 minutes before you walk in.

RAG vs fine-tuning? RAG: new knowledge, changeable, attributable. Fine-tuning: new behaviour or style, baked in, needs retraining.
Handle imbalanced data? SMOTE, class weights, threshold tuning, PR-AUC metric. Never SMOTE before the train-test split.
Batch norm vs layer norm? Batch norm across the batch (CNNs). Layer norm across features per sample (Transformers).
Data leakage? Using future or test information in training. Check: would this feature exist at prediction time in production?
SECTION 11 · REAL-LIFE CASE STUDIES

Realistic schemas, step-by-step solutions with complete code, business interpretation, and notes on what the interviewer is evaluating.

E-commerce funnel drop-off analysis · SQL
User retention cohort analysis · SQL
Detecting suspicious transaction patterns · SQL
Marketing channel revenue attribution · SQL
DAU/MAU ratio and power-user analysis · SQL
Customer churn: EDA + model building · Python
A/B test analysis: new checkout flow · Python
Product review sentiment classification · Python
+ a complete 3–5 hour take-home walkthrough
SECTION 13 · RESUME BULLET FORMULAS

Action verb + what you did + impact. Six templates, from Fresher to Research-Leaning.

“Built a churn model” → “Built a churn prediction model (XGBoost, AUC=0.85) that reduced customer attrition by 22%, saving $440K annually.”
ATS rules and what breaks parsing
Section order for freshers, 3–8 years, and 8+ years
Bullet banks for ML, analytics, engineering and GenAI/LLM work
Cover letter and LinkedIn “About” templates

Excerpts are shown as text from the finished PDF. Full answers, code and follow-ups are in the guide.

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All 209 questions across 14 sections · 234 pages
2–3 follow-up questions per question, each answered
71 interviewer-insight notes
8 worked case studies + a full take-home walkthrough
The 30-day prep plan and the last-minute revision bank
6 resume templates, cover letter and LinkedIn templates
Formula cheat sheet, glossary and curated resources
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SECTION 02
Machine Learning
Fundamentals, core algorithms, feature engineering, ensembles and production ML.
35 Q&A · 29 pages
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SECTION 03
Deep Learning
Neural networks, CNNs, RNNs, LSTMs, Transformers and transfer learning.
30 Q&A · 25 pages
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SECTION 04
AI / GenAI & LLMs
LLM internals, prompt engineering, RAG, fine-tuning, LoRA and AI agents.
30 Q&A · 26 pages
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SECTION 05
SQL
Joins, window functions, CTEs, optimisation and real-world query patterns.
31 Q&A · 30 pages
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SECTION 06
Python
Core Python, Pandas, NumPy, scikit-learn and applied coding challenges.
30 Q&A · 30 pages
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SECTION 07
Statistics & Probability
Distributions, CLT, p-values, confidence intervals and Bayesian thinking.
25 Q&A · 22 pages
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SECTION 08
A/B Testing & Experimentation
Test design, sample size, peeking, CUPED, metric selection and pitfalls.
10 Q&A · 7 pages
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SECTION 09
ML System Design
Fraud detection, recommendations, ETA, search ranking, moderation, churn.
8 Q&A · 6 pages
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SECTION 10
Behavioral / HR (STAR)
Project walkthroughs, conflict, ownership and stakeholder communication.
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SECTION 11
Real-Life Case Studies
5 SQL and 3 Python case studies with full code, plus a take-home walkthrough.
8 case studies · 27 pages
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LEVEL-TAGGED

Written for the level you are actually interviewing at

Every question carries a Fresher, Mid-Level or Senior tag, so you know what depth is expected of you before you answer.

FRESHER
Freshers, interns and career switchers: fundamentals, definitions with practical framing, project walkthroughs, resume built around projects.
MID-LEVEL
Applied ML, feature engineering, production issues, experimentation and full case studies with code.
SENIOR
ML system design, trade-off reasoning, advanced GenAI topics, org-level impact and leadership framing.
COMPANY COVERAGE

50+ companies, grouped by archetype

Questions are tagged with the companies where candidates reported them, and the revision section regroups everything by company type so you can prepare for the interview you actually have.

FAANG / PRODUCT
Amazon, Google, Meta, Microsoft, Apple, Netflix, Uber, Airbnb, Spotify, LinkedIn, Salesforce
INDIAN IT SERVICES
TCS, Infosys, Wipro, Cognizant, Capgemini, HCL, Tech Mahindra, Genpact
UNICORNS / CONSUMER TECH
Flipkart, Swiggy, Zomato, Meesho, CRED, Dream11, Paytm
FINTECH / BANKING
PhonePe, Razorpay, JPMorgan, Goldman Sachs, Capital One
CONSULTING
Deloitte, EY, KPMG, PwC, McKinsey QuantumBlack, BCG X

StepUpDataLab is not affiliated with, sponsored by or endorsed by any company named above. Company names indicate where questions were reported by candidates.

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