Crack the top 40 machine learning interview questions

Crack the top 40 machine learning interview questions

The machine learning interview is a rigorous process. Today, take a deep dive into the top 40 machine learning interview questions for FAANG companies.

Machine learning (ML) is the future of our world. In years to come, nearly every product will include ML components. ML is projected to grow from $7.3B in 2020 to $30.6B in 2024. This demand for ML skills is pervasive across the industry.

The machine learning interview is a rigorous process where candidates are assessed both for their knowledge of basic concepts and for understanding of ML systems, real-world applications, and product-specific demands.

If you are looking for a career in machine learning, it is crucial to understand what is expected in the interview. So, to help you prepare, I have collected the top 40 machine learning interview questions. We will begin with some of the basics and then move to advanced questions.

Today we will go over:

  • Machine learning interview overview
  • Company specific processes
  • Beginner Questions (10)
  • Intermediate Questions (15)
  • Advanced Questions (10)
  • Product-specific Questions (5)
  • What to learn next

Machine learning interview overview

Machine learning interview questions are an integral part of becoming a data scientist, machine learning engineer, or data engineer. Depending on the company, the job description title for a Machine Learning engineer may differ. You can expect to see titles like Machine Learning Engineer, Data Scientist, AI Engineer, and more.

Companies hiring for machine learning roles conduct interviews to assess individual abilities in various areas. ML interview questions tend to fall into one of these four categories.

  • Algorithms and ML theory: How algorithms compare, how to measure them accurately
  • Programming skills: Usually Python or domain-specific languages
  • Interest in machine learning: Industry trends and your vision for ML components of the future
  • Industry or product specific questions: How you take general ML knowledge and apply it to specific products

ML interview questions now focus heavily on system design. In the ML system design interview portion, candidates are given open-ended ML problems and are expected to build an end-to-end machine learning system. Common examples are recommendation systems, visual understanding systems, and search-ranking systems.

_To learn more about how to solve these problems, check out our article [The Anatomy of a Machine Learning Interview Question_](https://www.educative.io/blog/anatomy-machine-learning-system-design-interview)

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