Wipro

Global information technology, consulting and business process services company.

4 Rounds ~21 Days Medium

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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 Data Scientist 8 Machine Learning Engineer 7

All Topics Machine Learning 8 System Design 4 SQL 3 Deep Learning 3 Data Manipulation 2 Big Data 2 MLOps 2 Statistics 2

Data Scientist • Technical • medium

Explain the difference between Bagging and Boosting. How does the XGBoost algorithm work under the hood?

#Ensemble Methods #XGBoost #Decision Trees

Practice

Data Scientist • Technical • medium

You are building a fraud detection model for a banking client where the fraud cases are less than 0.1% of the data. How do you handle this extreme class imbalance?

#Imbalanced Data #SMOTE #Evaluation Metrics

Practice

Data Scientist • Technical • easy

Explain the Bias-Variance Tradeoff. How do you diagnose if a model deployed for a client is overfitting?

#Model Evaluation #Overfitting #Statistics

Practice

Data Scientist • Technical • medium

How does a Random Forest model calculate feature importance?

#Random Forest #Feature Engineering #Interpretability

Practice

Data Scientist • Technical • hard

Explain the mathematical intuition behind Logistic Regression. Why do we use Log-Loss instead of Mean Squared Error (MSE) as the cost function?

#Logistic Regression #Loss Functions #Optimization

Practice

Data Scientist • Technical • medium

What is the difference between L1 (Lasso) and L2 (Ridge) regularization? In what client scenario would you prefer L1 over L2?

#Regularization #Feature Selection #Linear Models

Practice

Data Scientist • Technical • easy

Explain the working of the K-Means clustering algorithm. How do you determine the optimal number of clusters (K)?

#Unsupervised Learning #Clustering #K-Means

Practice

Data Scientist • Technical • medium

Explain the concept of Data Leakage in machine learning. Give an example of how it might occur in a client's churn prediction model.

#Data Leakage #Model Validation #Feature Engineering

Practice

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Senior Engineer

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