LTIMindtree
Global technology consulting and digital solutions 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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Coding
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medium
Implement a custom cross-validation split function in Python for time-series data without using scikit-learn's TimeSeriesSplit.
#Python
#Time Series
#Cross Validation
#Algorithms
Data Scientist
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Technical
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medium
What evaluation metrics would you use for a multi-class classification problem where the classes are highly imbalanced?
#Metrics
#Multi-class
#Imbalanced Data
Data Scientist
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Technical
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medium
Explain the bias-variance tradeoff. How does this concept apply differently to Random Forests compared to Gradient Boosting Machines?
#Theory
#Ensemble Methods
#Model Evaluation
Data Scientist
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Technical
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medium
We are building a fraud detection model for a banking client where the fraud rate is 0.01%. How do you handle this highly imbalanced dataset?
#Imbalanced Data
#SMOTE
#Class Weights
#Evaluation Metrics
Data Scientist
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Technical
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hard
Explain the mathematical intuition behind Support Vector Machines (SVM) and the kernel trick. When would you use an RBF kernel over a linear kernel?
#SVM
#Mathematics
#Kernels
Data Scientist
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Technical
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medium
What is the difference between L1 (Lasso) and L2 (Ridge) regularization? How do they affect feature selection?
#Regularization
#Feature Selection
#Linear Models
Data Scientist
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Technical
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medium
How does XGBoost handle missing values internally without requiring explicit imputation beforehand?
#XGBoost
#Missing Data
#Tree Algorithms
Data Scientist
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Technical
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easy
Explain the fundamental difference between bagging and boosting ensemble methods.
#Ensemble Methods
#Bagging
#Boosting
Data Scientist
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Technical
•
medium
How do you choose the optimal number of clusters in a K-Means algorithm? Explain how the Silhouette score works.
#Clustering
#K-Means
#Evaluation Metrics
Machine Learning Engineer
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Technical
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easy
What is the difference between L1 (Lasso) and L2 (Ridge) regularization? In what scenario would you strictly prefer L1?
#Regularization
#Linear Models
#Feature Selection
Machine Learning Engineer
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Technical
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easy
How do you choose the optimal number of clusters in K-Means clustering? Explain the Elbow method and Silhouette score.
#Unsupervised Learning
#Clustering
Machine Learning Engineer
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Technical
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easy
When evaluating a binary classifier for a rare disease detection system, would you prioritize Precision or Recall? Why? What metric combines both?
#Evaluation Metrics
#Classification
Machine Learning Engineer
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Technical
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medium
How does Principal Component Analysis (PCA) differ from t-SNE? When would you use one over the other?
#Dimensionality Reduction
#PCA
#t-SNE
Machine Learning Engineer
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Technical
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medium
How do Explainable AI (XAI) frameworks like SHAP or LIME work? Why is explainability important for our enterprise clients?
#Explainable AI
#SHAP
#LIME
Machine Learning Engineer
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Technical
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medium
Explain the mathematical difference between Random Forest and Gradient Boosting. Why might XGBoost overfit more easily than Random Forest?
#Ensemble Methods
#Decision Trees
#Overfitting
Machine Learning Engineer
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Technical
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medium
How do you handle highly imbalanced datasets in a classification problem? Explain the pros and cons of SMOTE versus class weighting.
#Data Preprocessing
#Imbalanced Data
#Classification
Difficulty Radar
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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.