Add office photos
Microsoft Corporation logo
Employer?
Claim Account for FREE

Microsoft Corporation

4.0
based on 1.7k Reviews
Video summary
Proud winner of ABECA 2024 - AmbitionBox Employee Choice Awards
Filter interviews by
Data Scientist
Skills
Clear (1)

Microsoft Corporation Data Scientist Interview Questions and Answers

Updated 1 Jul 2024

Q1. How do you work towards a random forest?

Ans.

To work towards a random forest, you need to gather and preprocess data, select features, train individual decision trees, and combine them into an ensemble.

  • Gather and preprocess data from various sources

  • Select relevant features for the model

  • Train individual decision trees using the data

  • Combine the decision trees into an ensemble

  • Evaluate the performance of the random forest model

View 1 answer
right arrow

Q2. check technical stack, whether you have the right tech skills

Ans.

Yes, I have the right tech skills for the Data Scientist role.

  • Proficient in programming languages like Python, R, and SQL

  • Experience with data visualization tools like Tableau or Power BI

  • Knowledge of machine learning algorithms and statistical analysis techniques

  • Familiarity with big data technologies like Hadoop and Spark

Add your answer
right arrow

Q3. What is bias variance trade-off

Ans.

Bias-variance trade-off is the balance between overfitting and underfitting in a model.

  • Bias is the error due to assumptions made in the learning algorithm. Variance is the error due to sensitivity to small fluctuations in the training set.

  • High bias leads to underfitting, while high variance leads to overfitting.

  • The goal is to find the sweet spot where the model has low bias and low variance, which results in good generalization performance.

  • Regularization techniques like Lasso...read more

Add your answer
right arrow

Q4. How will you finetune LLMs

Ans.

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 dataset for specific tasks.

  • Regularly evaluate the model's...read more

Add your answer
right arrow
Discover Microsoft Corporation interview dos and don'ts from real experiences

Q5. Explain L1 & L2 regularization

Ans.

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 coefficients towards zero

  • Both L1 and L2 regularization help...read more

Add your answer
right arrow

Q6. 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 categorical data.

  • They can be prone to overfitting, which ca...read more

Add your answer
right arrow

Q7. 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 parallel processing capabilities.

  • It can be used for classificati...read more

Add your answer
right arrow

Q8. Explain error metric

Ans.

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 understanding the accuracy and reliability of the model's pre...read more

Add your answer
right arrow

More about working at Microsoft Corporation

Back
Awards Leaf
AmbitionBox Logo
Top Rated Internet/Product Company - 2024
Awards Leaf
Contribute & help others!
Write a review
Write a review
Share interview
Share interview
Contribute salary
Contribute salary
Add office photos
Add office photos

Interview Process at Microsoft Corporation Data Scientist

based on 8 interviews
3 Interview rounds
Resume Shortlist Round
Technical Round - 1
Technical Round - 2
View more
interview tips and stories logo
Interview Tips & Stories
Ace your next interview with expert advice and inspiring stories

Top Data Scientist Interview Questions from Similar Companies

TCS Logo
3.7
 • 29 Interview Questions
Affine Logo
3.3
 • 18 Interview Questions
Accenture Logo
3.8
 • 11 Interview Questions
View all
Recently Viewed
COMPANY BENEFITS
Novo Nordisk
139 benefits
REVIEWS
PC Solutions
3.7
(324 reviews)
SALARIES
FIS
SALARIES
Microsoft Corporation
SALARIES
Vinculum Solutions
JOBS
Infosys
No Jobs
SALARIES
HCLTech
LIST OF COMPANIES
Eurofins It Solutions
Locations
SALARIES
JoulestoWatts Business Solutions
INTERVIEWS
Infosys
10 top interview questions
Share an Interview
Stay ahead in your career. Get AmbitionBox app
play-icon
play-icon
qr-code
Helping over 1 Crore job seekers every month in choosing their right fit company
75 Lakh+

Reviews

5 Lakh+

Interviews

4 Crore+

Salaries

1 Cr+

Users/Month

Contribute to help millions

Made with ❤️ in India. Trademarks belong to their respective owners. All rights reserved © 2024 Info Edge (India) Ltd.

Follow us
  • Youtube
  • Instagram
  • LinkedIn
  • Facebook
  • Twitter