Microsoft Corporation Data Scientist Interview Questions, Process, and Tips for Experienced

Q1. Explain Decision Trees
Ans. 
Decision Trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision Trees are a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• They are easy to interpret and visualize, making them popular for exploratory data analysis.

• Decision Trees can handle both numerical and c...read more
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Q2. How will you finetune LLMs
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LLMs can be finetuned by adjusting hyperparameters, training on specific datasets, and using techniques like transfer learning.
• Adjust hyperparameters such as learning rate, batch size, and number of layers to improve performance.

• Train the LLM on domain-specific datasets to improve its understanding of specialized language.

• Utilize transfer learning by starting with a pre-trained LLM model and fine-tuning it on a smaller...read more
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Based on topological sort

Q1. Ml baiscs like LR, SVM
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Q1. Ml design questions were asked
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Q1. Why do you want to join
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Q1. Explain L1 & L2 regularization
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L1 & L2 regularization are techniques used in machine learning to prevent overfitting by adding a penalty term to the cost function.
• L1 regularization adds the absolute values of the coefficients as penalty term (Lasso regression)

• L2 regularization adds the squared values of the coefficients as penalty term (Ridge regression)

• L1 regularization encourages sparsity in the model, while L2 regularization tends to shrink the c...read more
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Q2. Explain error metric
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Error metric is a measure used to evaluate the performance of a model by comparing predicted values to actual values.
• Error metric quantifies the difference between predicted values and actual values.

• Common error metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared.

• Lower values of error metric indicate better performance of the model.

• Error metric helps in und...read more
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Q3. Where do you see yourself
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2 coding questions - one on binary tree and list operations.

Q1. Xgboost explanation
Ans. 
Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
• Xgboost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosted decision trees.

• It is widely used in Kaggle competitions and other machine learning tasks due to its efficiency and accuracy.

• Xgboost is known for its ability to handle missing data, regularization techniques, and parall...read more
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Q2. Data structures question compulsory
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