.svg)
.svg)
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!
If you are preparing for a tech interview, check out our technical interview checklist, interview questions page, and salary negotiation ebook to get interview-ready!
Having trained over 12,000 software engineers, we know what it takes to crack the toughest tech interviews. Our alums consistently land offers from FAANG+ companies. The highest ever offer received by an IK alum is a whopping $1.267 Million!
At IK, you get the unique opportunity to learn from expert instructors who are hiring managers and tech leads at Google, Facebook, Apple, and other top Silicon Valley tech companies.
Want to nail your next tech interview? Sign up for our FREE Webinar.
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:
- Sample Facebook Machine Learning Interview Questions and Answers
- Top Facebook Machine Learning Interview Questions for Practice
- Facebook Machine Learning Interview Questions for Experienced Professionals
- FAQs on Facebook Machine Learning Interview Questions
Sample Facebook Machine Learning Interview Questions and Answers
We’ll begin with some sample Facebook machine learning interview questions and answers to get a basic idea of what to expect.
Q1. What is overfitting in Machine Learning?
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.
Q2. What is entropy in Machine Learning?
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.
Q3. What is VIF?
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.
Q4. How would you handle missing or corrupted data in a dataset?
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.
Q5. What are some tests for checking the normality of a dataset?
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.
Top Facebook Machine Learning Interview Questions for Practice
Here are some Facebook machine learning interview questions. Take a jab and see if you can solve them before your interview:
- Give me an example of a challenging ML project.
- How would you evaluate an offline model's performance?
- If a model performed poorly after launching, what potential causes or issues do you suspect happened in the model training step?
- What does a ROC area under the curve as an integral represent?
- What are the advantages and disadvantages of SVM?
- Explain the KNN algorithm.
- Differentiate between linear regression and logistic regression.
- How would you get a CCA objective function from PCA?
- What are some ways to split a tree in a decision tree algorithm?
- Explain how pruning works.
Want to practice more questions? Check out our list of:
Facebook Machine Learning Interview Questions for Experienced Professionals
Lastly, here are some Facebook machine learning interview questions for experienced professionals:
- Given several images of some cats and dogs, develop a model to identify if a picture contains a cat or a dog
- Find the probability of a user considering a given ad relevant and useful
- Given a function that returns whether a git commit contains a bug or not, find the first git commit that contains a bug.
- Write a function to determine if a string s1 is another string s3's permutation
- Find the local minimum point(s) from an array
- Design a newsfeed.
- Design Facebook
- Design Facebook Messenger
- Design Instagram
- Design Facebook’s live post comment updates
- Find the probability of a user clicking on a given post
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.
FAQs on Facebook Machine Learning Interview Questions
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.
Ready to Nail Your Next ML Interview?
Interview Kickstart’s Machine Learning Engineering Interview Course is designed and taught by ML experts from FAANG and Tier-1 tech companies. These courses are tailored to help ML engineers nail the most challenging tech interviews.
If you’re looking for guidance and help with getting started, sign up for our FREE webinar. As pioneers in technical interview preparation, we have trained thousands of software engineers to crack the most challenging coding interviews and land jobs at their dream companies, such as Google, Facebook, Apple, Netflix, Amazon, and more!
Sign up now!
.png)
