Cognizant
American multinational information technology services and consulting 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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easy
Explain the Bias-Variance tradeoff. How do you know if your model is suffering from high bias or high variance?
#Model Evaluation
#Overfitting
#Underfitting
Data Scientist
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Technical
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medium
We are building a credit card fraud detection model for a BFSI client. The positive class (fraud) is only 0.1% of the data. How do you approach this problem?
#Imbalanced Data
#SMOTE
#Evaluation Metrics
Data Scientist
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Technical
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medium
Compare Random Forest and Gradient Boosting. In what scenarios would you choose one over the other?
#Ensemble Methods
#Bagging
#Boosting
Data Scientist
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Technical
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hard
Explain how L1 (Lasso) and L2 (Ridge) regularization work. Why does L1 lead to sparsity?
#Regularization
#Feature Selection
#Mathematics
Data Scientist
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Technical
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medium
You have segmented a client's customer base using K-Means clustering, but you have no ground truth labels. How do you evaluate the quality of your clusters?
#Unsupervised Learning
#Clustering
#Metrics
Data Scientist
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Technical
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hard
Walk me through the mathematical formulation of Logistic Regression. How are the coefficients updated during training?
#Mathematics
#Optimization
#Gradient Descent
Data Scientist
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Technical
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medium
What is data leakage in machine learning? Give an example of how it might happen during feature engineering and how to prevent it.
#Model Validation
#Feature Engineering
#Best Practices
Data Scientist
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Technical
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medium
How do you detect and deal with multicollinearity in a multiple linear regression model?
#Statistics
#Regression
#Feature Selection
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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