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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medium
How do you detect and deal with multicollinearity in a multiple linear regression model?
#Statistics
#Regression
#Feature Selection
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
Machine Learning Engineer
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Coding
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medium
Implement a basic Linear Regression algorithm from scratch using only NumPy.
#Python
#NumPy
#Mathematics
#Gradient Descent
Machine Learning Engineer
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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 happen during feature engineering.
#Data Leakage
#Model Evaluation
#Feature Engineering
Machine Learning Engineer
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Technical
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medium
We are building a fraud detection model for a banking client where the fraudulent transactions are less than 0.1% of the data. How do you handle this extreme class imbalance?
#Imbalanced Data
#SMOTE
#Class Weights
#Evaluation Metrics
Machine Learning Engineer
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Technical
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easy
Explain the Bias-Variance tradeoff. How does increasing the depth of a Decision Tree affect bias and variance?
#Model Evaluation
#Decision Trees
#Overfitting
Machine Learning Engineer
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Technical
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medium
What is the difference between Random Forest and Gradient Boosting? When would you choose one over the other for a client project?
#Ensemble Methods
#Bagging
#Boosting
Machine Learning Engineer
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Technical
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medium
Explain L1 (Lasso) and L2 (Ridge) regularization. Which one would you use if you wanted to perform feature selection?
#Regularization
#Feature Selection
#Mathematics
Machine Learning Engineer
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Technical
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easy
For a healthcare client predicting cancer from scans, which evaluation metric is more important: Precision or Recall? Why?
#Evaluation Metrics
#Domain Knowledge
Machine Learning Engineer
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Technical
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medium
How do you determine the optimal number of clusters (K) in a K-Means clustering algorithm?
#Unsupervised Learning
#Clustering
Machine Learning Engineer
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Technical
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medium
A client complains that your machine learning model is a 'black box'. How do you explain the model's predictions to non-technical stakeholders?
#Explainable AI
#SHAP
#LIME
Difficulty Radar
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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.