Wipro
Global information technology, consulting and business process services company.
4 Rounds
~21 Days
Medium
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
Data Scientist
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Technical
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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
Data Scientist
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Technical
•
medium
Explain the difference between Bagging and Boosting. How does the XGBoost algorithm work under the hood?
#Ensemble Methods
#XGBoost
#Decision Trees
Data Scientist
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Technical
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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
Data Scientist
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Technical
•
easy
Explain the Bias-Variance Tradeoff. How do you diagnose if a model deployed for a client is overfitting?
#Model Evaluation
#Overfitting
#Statistics
Data Scientist
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Technical
•
medium
How does a Random Forest model calculate feature importance?
#Random Forest
#Feature Engineering
#Interpretability
Data Scientist
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Technical
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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
Data Scientist
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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
Data Scientist
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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
Machine Learning Engineer
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Technical
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easy
What are the differences between L1 (Lasso) and L2 (Ridge) regularization? When would you choose to use one over the other?
#Regularization
#Linear Models
#Feature Selection
Machine Learning Engineer
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Technical
•
medium
How does the XGBoost algorithm handle missing values internally during training?
#Tree Models
#XGBoost
#Missing Data
Machine Learning Engineer
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Technical
•
easy
What is the fundamental difference between a discriminative model and a generative model?
#Statistics
#Generative AI
#Classification
Machine Learning Engineer
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Technical
•
medium
Explain the ROC curve and AUC. In what specific scenario would you prefer using Precision-Recall AUC over ROC AUC?
#Evaluation Metrics
#Classification
Machine Learning Engineer
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Technical
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hard
What is SMOTE, and what are its limitations when dealing with high-dimensional data or text data?
#Imbalanced Data
#SMOTE
#High Dimensionality
Machine Learning Engineer
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Technical
•
medium
How do you handle categorical variables with extremely high cardinality in a tree-based model versus a linear model?
#Feature Engineering
#Categorical Data
#Modeling
Machine Learning Engineer
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Technical
•
medium
How do you handle highly imbalanced datasets when building a fraud detection model for a banking client?
#Imbalanced Data
#Classification
#Fraud Detection
Difficulty Radar
Based on recent AI-sourced data.
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The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.