Infosys
Global leader in next-generation digital services and consulting.
3 Rounds
~14 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
Machine Learning Engineer
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Technical
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medium
Explain the bias-variance tradeoff. How do Random Forest and Gradient Boosting algorithms handle this tradeoff differently?
#Ensemble Methods
#Model Evaluation
Machine Learning Engineer
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Technical
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medium
In a recent fraud detection project for a financial client, the dataset had a 99:1 class imbalance. How would you handle this highly imbalanced dataset?
#Imbalanced Data
#Classification
Machine Learning Engineer
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Technical
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medium
What is the difference between L1 (Lasso) and L2 (Ridge) regularization? When would you choose one over the other in a consulting project?
#Regularization
#Linear Models
Machine Learning Engineer
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Technical
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hard
Explain how XGBoost works under the hood. What makes it faster and more accurate than traditional Gradient Boosting?
#Ensemble Methods
#XGBoost
Machine Learning Engineer
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Technical
•
medium
Explain the mathematical intuition behind Principal Component Analysis (PCA). How do you decide the number of principal components to keep?
#Dimensionality Reduction
#Math
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
SimulateUnwritten Rules
Think Out Loud
Always explain your thought process before writing code or drawing architecture.