Microsoft Corporation Data Scientist 2 Interview Questions and Answers

Q1. How does fbpropher forecasting model works and how is can be used to forecsst trafffic
Ans. 
fbprophet is a forecasting model developed by Facebook that uses time series data to make predictions.
• fbprophet is an open-source forecasting tool developed by Facebook's Core Data Science team.

• It is based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

• fbprophet can be used to forecast traffic by providing historical data on traffic patterns and usi...read more
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Q1. How can Logistic regression be applied for multiclasstext classification
Ans. 
Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.
• One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.

• Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.

• Evaluate the model using appropriate...read more
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Q1. Mostly related to Projects and JD
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Q1. DSA Based question on Sliding window
Ans. 
Sliding window technique efficiently solves problems involving contiguous subarrays or substrings.
• Used to find maximum sum of 'k' consecutive elements in an array. Example: For array [1, 2, 3, 4, 5] and k=3, max sum is 12.

• Helps in finding the longest substring without repeating characters. Example: In 'abcabcbb', the longest is 'abc' with length 3.

• Can be applied to problems like finding the smallest subarray with a sum...read more
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Q2. CNN, LMMs, etc.
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Q1. How to ensure scalability
Ans. 
Scalability ensures systems can handle growth in data and users efficiently without performance loss.
• Use cloud services like AWS or Azure for elastic scaling based on demand.

• Implement microservices architecture to allow independent scaling of components.

• Optimize algorithms and data structures for performance; for example, using hash tables for quick lookups.

• Leverage distributed computing frameworks like Apache Spark fo...read more
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2 DSA Codes were there with python

Q1. Explain your project
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Q1. Tell me about your project
Ans. 
Developed a machine learning model to predict customer churn for a telecom company
• Used Python and scikit-learn for data preprocessing and model building

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall

• Implemented the model in a production environment for real-time predictions
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Q1. What is p-value
Ans. 
A p-value measures the strength of evidence against the null hypothesis in statistical hypothesis testing.
• A p-value ranges from 0 to 1, with lower values indicating stronger evidence against the null hypothesis.

• Common thresholds for significance are 0.05, 0.01, and 0.001.

• For example, a p-value of 0.03 suggests there is a 3% probability of observing the data if the null hypothesis is true.

• A p-value does not indicate the...read more
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Q2. How would you treat outlier
Ans. 
Outliers can skew data analysis; handling them involves detection, assessment, and appropriate treatment methods.
• Identify outliers using statistical methods like Z-scores or IQR (Interquartile Range).

• Visualize data with box plots or scatter plots to spot outliers easily.

• Assess the impact of outliers on your analysis; determine if they are errors or valid observations.

• Consider transforming data (e.g., log transformation...read more
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