Microsoft Corporation Data Scientistoncampusinterview Interview Questions and Answers

Q1. Asked Algorithms used in the project (No follow-up on mentioned algorithms, cut-off mid-explanation of business problem, scale, and solution wanting to know just the names of the algorithm) - Answered by n...read more
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Q2. Count all pairs of numbers from a list where the ending digit of the ith number equals the starting digit of the jth number. Example [122, 21, 21, 23] should have 5 pairs (122, 21), (122, 21), (122, 23), (...read more
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Count pairs of numbers where ending digit of ith number equals starting digit of jth number.
• Iterate through each pair of numbers in the list

• Check if the ending digit of the ith number equals the starting digit of the jth number

• Increment the count if the condition is met
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Q3. Interpretation of graphs, the first graph had perpendicular lines from the error to the fitted line and the second graph had lines from the error to the fitted line, parallel to the y-axis. - Interpreted t...read more
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Interpretation of graphs in linear regression analysis
• Perpendicular lines from error to fitted line in first graph indicate OLS using projection matrices

• Lines parallel to y-axis from error to fitted line in second graph suggest evaluation of linear regression to y-pred - y-actual method

• PCA could also be a possible interpretation for the second graph
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Q4. What does np.einsum() do
Ans. 
np.einsum() performs Einstein summation on arrays.
• Performs summation over specified indices

• Can also perform other operations like multiplication, contraction, etc.

• Syntax: np.einsum(subscripts, *operands)
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Q5. How to generate random numbers using numpy, what is the difference between numpy.random.rand and numpy.random.randn
Ans. 
numpy.random.rand generates random numbers from a uniform distribution, while numpy.random.randn generates random numbers from a standard normal distribution.
• numpy.random.rand generates random numbers from a uniform distribution between 0 and 1.

• numpy.random.randn generates random numbers from a standard normal distribution with mean 0 and standard deviation 1.

• Example: np.random.rand(3, 2) will generate a 3x2 array of r...read more
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Q6. Difference between logit and probabilities in deep learning
Ans. 
Logit is the log-odds of the probability, while probabilities are the actual probabilities of an event occurring.
• Logit is the natural logarithm of the odds ratio, used in logistic regression.

• Probabilities are the actual likelihood of an event occurring, ranging from 0 to 1.

• In deep learning, logit values are transformed into probabilities using a softmax function.

• Logit values can be negative or positive, while probabili
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Q1. Basicn details to check for qualifications
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Q1. About my projects
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Q1. More details about ML models
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Q1. Why did you choose infor
Ans. 
I chose Infor because of its reputation for innovative technology solutions and its commitment to employee development.
• Infor is known for its cutting-edge technology solutions in the industry.

• I was impressed by Infor's focus on employee growth and development opportunities.

• I believe Infor's values align with my own professional goals and aspirations.
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Q2. Explain how you work under stress
Ans. 
I thrive under pressure by staying organized, prioritizing tasks, and maintaining a positive attitude.
• I stay organized by creating to-do lists and breaking down tasks into manageable steps.

• I prioritize tasks based on deadlines and importance to ensure that critical work is completed first.

• I maintain a positive attitude by taking short breaks to recharge, practicing deep breathing exercises, and seeking support from col
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Q1. Everything mentioned on CV
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Q2. Some coding questions
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Q1. Same questions as first interview but deeper approach
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Q2. Coding is involved again
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Q1. How to ensure scalability
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Q1. How do you preprocess large/small dataset
Ans. 
Preprocessing large/small datasets involves cleaning, transforming, and organizing data to prepare it for analysis.
• Remove duplicates and missing values

• Normalize or standardize numerical features

• Encode categorical variables

• Feature scaling

• Handling outliers

• Dimensionality reduction techniques like PCA

• Splitting data into training and testing sets
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Q2. Data augmentation
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SQL coding test on HackerRank. Also some questions on previous experience

Case study on a data project

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. Basic DS question like how to handle missing features
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Q1. Case study based questions
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