Xcaliber Infotech ml engineer Interview Questions and Answers

Q1. Explain bias variance tradeoff?
Ans. 
Bias-variance tradeoff is the balance between underfitting and overfitting in machine learning models.
• Bias refers to the error introduced by approximating a real-world problem, variance refers to the error introduced by modeling the noise in the training data.

• High bias can cause underfitting, where the model is too simple to capture the underlying patterns in the data.

• High variance can cause overfitting, where the mode...read more
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Q2. Explain PCA and feature selection technique ?
Ans. 
PCA is a dimensionality reduction technique that transforms data into a lower-dimensional space. Feature selection is the process of selecting a subset of relevant features for use in model training.
• PCA helps in reducing the dimensionality of data by finding the principal components that explain the most variance in the data.

• Feature selection involves selecting the most important features from the dataset based on cert...read more
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Q3. Explain difference between delete and truncate?
Ans. 
Delete removes rows one by one, while truncate removes all rows at once.
• Delete is a DML command and can be rolled back, while truncate is a DDL command and cannot be rolled back.

• Delete triggers delete triggers and fires delete triggers, while truncate does not trigger any triggers.

• Delete is slower as it logs individual row deletions, while truncate is faster as it logs the deallocation of the data pages.

• Delete can have...read more
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Q4. Explain your project in details?
Ans. 
Developed a machine learning model to predict customer churn in a telecom company.
• Used historical customer data to train the model

• Features included customer demographics, usage patterns, and customer service interactions

• Implemented a random forest algorithm for prediction

• Achieved an accuracy of 85% on test data
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Q5. How you have deployed your model and how to monitor the deployed model?
Ans. 
I have deployed models using cloud services like AWS SageMaker and monitored them using tools like Prometheus and Grafana.
• Deployed models using AWS SageMaker for easy scalability and management

• Utilized Prometheus and Grafana for monitoring model performance and health

• Set up alerts for abnormal behavior or performance degradation

• Regularly reviewed logs and metrics to ensure model is functioning as expected
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Implement algorithm from scratch