Google

Leading technology company specializing in search, cloud, and AI.

4 Rounds ~21 Days Very Hard

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The Interview Loop

Recruiter Screen (30 min)

Standard fit check, behavioral questions, and resume overview.

Technical Loop (3-4 Rounds)

Deep dive into domain knowledge, coding, and system design.

Interview Question Bank

All Roles AI Engineer 47 Cloud Engineer 65 Data Analyst 43 Data Engineer 76 Data Scientist 53 Machine Learning Engineer 15 ML Engineer 52 Product Manager 15 Software Engineer 33

All Topics Statistics 8 Experimentation 6 Deep Learning 5 SQL 4 Machine Learning 4 ML Systems 3 NLP 3 Model Evaluation 2

Data Scientist • Behavioral • medium

Tell me about a data science project where the results surprised you. What did you do?

#Analytical Thinking

Practice

Data Scientist • Behavioral • medium

Describe how you communicated a complex model result to a non-technical stakeholder.

#Storytelling

Practice

Data Scientist • Behavioral • hard

Tell me about a time you had to push back on a business request for an analysis that would be misleading.

#Ethics #Communication

Practice

Data Scientist • Behavioral • medium

Describe a project where you had to iterate significantly on your initial approach.

#Iteration #Learning

Practice

Data Scientist • Behavioral • medium

How do you prioritize between multiple data science requests from different teams?

#Stakeholder Management

Practice

Data Scientist • Behavioral • hard

Tell me about a time your model failed in production. What did you learn?

#Production #MLOps

Practice

Data Scientist • Behavioral • medium

How do you approach ethical considerations in ML model building?

#Fairness #Bias

Practice

Data Scientist • Behavioral • hard

Describe a time you used data to challenge a widely held assumption in your organization.

#Influence #Analytics

Practice

Data Scientist • Coding • hard

Write a SQL query to calculate 30-day user retention.

#Retention #Analytics

Practice

Data Scientist • Coding • hard

How would you write a funnel analysis query in SQL?

#Funnel #Analytics

Practice

Data Scientist • Coding • medium

Write a query to identify duplicate records and deduplicate them.

#Deduplication #Data Quality

Practice

Data Scientist • System Design • hard

How would you build a recommendation system? Compare collaborative vs content-based filtering.

#Collaborative Filtering #Content-Based

Practice

Data Scientist • System Design • hard

Design a real-time fraud detection system for a payments platform.

#Fraud Detection #Real-Time ML

Practice

Data Scientist • System Design • hard

How would you build and deploy a churn prediction model?

#Churn #MLOps

Practice

Data Scientist • System Design • hard

Design a feature store. What are its key components?

#Feature Store #MLOps

Practice

Data Scientist • Technical • medium

Explain the bias-variance tradeoff. How does it influence model selection?

#Bias-Variance #Model Selection

Practice

Data Scientist • Technical • medium

What is a p-value? Why is a p-value of 0.05 not always sufficient?

#Hypothesis Testing #p-value

Practice

Data Scientist • Technical • medium

Explain the central limit theorem and its importance in data science.

#CLT #Sampling

Practice

Data Scientist • Technical • easy

What is the difference between Type I and Type II errors?

#Hypothesis Testing #Errors

Practice

Data Scientist • Technical • hard

How do you design an A/B test for a new product feature?

#A/B Testing #Statistics

Practice

Data Scientist • Technical • hard

What is the multiple testing problem? How do you correct for it?

#Bonferroni #FDR

Practice

Data Scientist • Technical • hard

Explain Bayesian vs Frequentist statistics. When would you use each?

#Bayesian #Frequentist

Practice

Data Scientist • Technical • medium

What is a confidence interval? How does it differ from a prediction interval?

#Confidence Interval #Intervals

Practice

Data Scientist • Technical • hard

Explain the curse of dimensionality and its implications for ML models.

#Dimensionality #Feature Engineering

Practice

Data Scientist • Technical • medium

How would you detect and handle multicollinearity in a regression model?

#Multicollinearity #Regression

Practice

Data Scientist • Technical • hard

Explain gradient boosting. How does XGBoost differ from a standard gradient boosting machine?

#Gradient Boosting #XGBoost

Practice

Data Scientist • Technical • medium

How does a Random Forest work? What are its hyperparameters and how do you tune them?

#Random Forest #Hyperparameter Tuning

Practice

Data Scientist • Technical • medium

What is regularization? Explain L1 vs L2 regularization and their effects.

#Regularization #L1 #L2

Practice

Data Scientist • Technical • medium

How do you handle class imbalance in a classification problem?

#Imbalanced Data #SMOTE

Practice

Data Scientist • Technical • medium

Explain the ROC curve and AUC metric. When would you prefer AUC over accuracy?

#ROC #AUC #Metrics

Practice

Data Scientist • Technical • medium

What is cross-validation? Explain k-fold and stratified k-fold.

#Cross Validation #k-Fold

Practice

Data Scientist • Technical • medium

How do you approach feature selection?

#Feature Selection #LASSO

Practice

Data Scientist • Technical • medium

Explain the difference between bagging and boosting.

#Bagging #Boosting

Practice

Data Scientist • Technical • medium

What is principal component analysis (PCA)? What are its limitations?

#PCA #SVD

Practice

Data Scientist • Technical • medium

Explain how backpropagation works.

#Backpropagation #Neural Networks

Practice

Data Scientist • Technical • hard

What is the vanishing gradient problem? How do LSTM and ResNet address it?

#LSTM #ResNet #Gradients

Practice

Data Scientist • Technical • hard

Explain the transformer architecture. What are attention mechanisms?

#Transformers #Attention #BERT

Practice

Data Scientist • Technical • medium

What is transfer learning? How would you fine-tune a pre-trained model?

#Transfer Learning #Fine-Tuning

Practice

Data Scientist • Technical • medium

How would you approach an NLP problem like sentiment analysis from scratch?

#Sentiment Analysis #Text Classification

Practice

Data Scientist • Technical • medium

What is embedding? How do word embeddings like Word2Vec and GloVe work?

#Embeddings #Word2Vec

Practice

Data Scientist • Technical • medium

Explain batch normalization and why it helps training.

#Batch Normalization #Training

Practice

Data Scientist • Technical • medium

How would you detect and mitigate overfitting in a neural network?

#Overfitting #Dropout #Regularization

Practice

Data Scientist • Technical • hard

How would you design an experiment to measure the impact of a new ranking algorithm?

#Experimentation #Metrics

Practice

Data Scientist • Technical • hard

What is a network effect in experimentation? How do you handle SUTVA violation?

#SUTVA #Network Effects

Practice

Data Scientist • Technical • medium

How do you choose a north star metric for a product?

#Metrics #Product Strategy

Practice

Data Scientist • Technical • easy

Explain the difference between a leading indicator and a lagging indicator.

#Metrics #KPIs

Practice

Data Scientist • Technical • hard

How would you identify the root cause of a sudden 20% drop in DAU?

#Root Cause Analysis #Debugging

Practice

Data Scientist • Technical • easy

What is an experiment holdout group?

#Holdout #Control Group

Practice

Data Scientist • Technical • easy

Explain the difference between INNER JOIN, LEFT JOIN, and CROSS JOIN.

#Joins #SQL

Practice

Data Scientist • Technical • hard

How do you monitor model performance in production? What is model drift?

#Model Drift #Monitoring

Practice

Data Scientist • Technical • hard

How do you evaluate the quality of a search ranking change at Google's scale?

#Search Ranking #Evaluation

Practice

Data Scientist • Technical • hard

Explain how Google's NDCG metric works for search relevance.

#NDCG #Relevance

Practice

Data Scientist • Technical • hard

What statistical techniques would you use to analyse Search CTR experiments?

#CTR #Statistics

Practice

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