Meta is one of the big five American IT companies with a keen interest in Machine Learning and Artificial Intelligence. ML and AI will play a crucial role in innovations in IT and our future as a civilization. Facebook values and hires machine learning engineers for the same reason.
The average salary of a machine learning engineer is $1,31,001 per annum, and the interviews at FAANG+ are competitive as expected. The interview process typically involves a phone screen, a technical interview, and an on-site interview. We’ve curated some Facebook machine learning interview questions to help you gauge your preparation level for your Facebook ML interview. Read ahead to learn more!
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This article focuses on Facebook machine learning interview questions to help you prepare for your next Facebook machine learning interview.
In this article, we’ll cover:
We’ll begin with some sample Facebook machine learning interview questions and answers to get a basic idea of what to expect.
When a machine tries to learn from an inadequate dataset, overfitting occurs. Hence overfitting can be seen as inversely proportional to the amount of data we have.
Entropy refers to the randomness in the data we want to process. The more entropy there is, the more difficult it is to derive useful insights from the data.
VIF, or the Variance Inflation Factor, measures the volume of multicollinearity in a collection of several regression variables. It can be calculated by taking the model’s variance and dividing it by the model’s variance with a single independent variable.
We can either drop the rows or columns with the missing or corrupted dataset or replace them entirely with a different value using IsNull(), dropna(), or Fillna() to handle this situation.
Shapiro-Wilk, Jarque-Bera, D’Agostino Skewness, Kolmogorov-Smirnov Test, and Anderson-Darling are some tests for checking the normality of a dataset.
Are you conflicted between being a data science engineer and a machine learning engineer? Our Machine Learning vs. Data Science — Which Has a Better Future article will help you decide what’s right for you.
Here are some Facebook machine learning interview questions. Take a jab and see if you can solve them before your interview:
Want to practice more questions? Check out our list of:
Lastly, here are some Facebook machine learning interview questions for experienced professionals:
We hope this list of Facebook ML interview questions will help you crack your tech interview. To prepare better, practice some mock interviews and be thorough with ML concepts.
The first step in making a good impression on your recruiter is to submit a strong resume. If you’ve been wondering how to create an ATS and recruiter-friendly resume, check out our Machine Learning Engineer Resume Guide, which includes tips, best formats, and a sample.
Q1. How do you explain a machine learning project in an interview?
Explain how you selected the project, the data source, project objective, dataset preparation, KPIs, baseline model, and the training process to explain a machine learning project in an interview.
Q2. What is the acceptance rate at Facebook?
The acceptance rate at Facebook is relatively low, especially for software engineers, at less than 3%.
Q3. What are the various types of machine learning?
Unsupervised, supervised, and reinforcement learning.
Q4. Are Facebook interviews difficult?
According to Glassdoor, Facebook interviews are rated 3.2 out of 5 in difficulty. So yes, Facebook interviews are reasonably challenging to crack.
Q5. How much does a Facebook machine learning engineer earn on average?
The average salary of a Meta machine learning engineer is $156,969, which is 14% above the national average for the US.
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The 11 Neural “Power Patterns” For Solving Any FAANG Interview Problem 12.5X Faster Than 99.8% OF Applicants
The 2 “Magic Questions” That Reveal Whether You’re Good Enough To Receive A Lucrative Big Tech Offer
The “Instant Income Multiplier” That 2-3X’s Your Current Tech Salary
The 11 Neural “Power Patterns” For Solving Any FAANG Interview Problem 12.5X Faster Than 99.8% OF Applicants
The 2 “Magic Questions” That Reveal Whether You’re Good Enough To Receive A Lucrative Big Tech Offer
The “Instant Income Multiplier” That 2-3X’s Your Current Tech Salary
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