Google AI Interview Questions— Acing the AI Interview

Google has some of the most talented AI research scientists, data engineers and data scientists in the world. Sundar Pichai the CEO of Google has focused to realign Google into an AI-first company. Google has weaved AI into all or most of its products from Gmail to Autonomous driving with the mass of data it possess.

At Acing AI, the aim is to help you to get into Data Science and AI. I have profiled some of the best technology companies and written articles about AI interviews at Microsoft, Amazon, Netflix, LinkedIn, Ebay, Twitter, Walmart, Apple, Facebook, Zillow, Salesforce, Uber, Intel, Adobe Tesla and most recently IBM. This has led to being the top writer in Artificial Intelligence on Medium. The AI interview preparation guides Part 1, Part 2 go over the details which help you ace any AI interview. Acing AI Portfolios helps you to showcase your AI work. Expert interviews and analyses gives you a sneak peak into the lives of AI/Data Science Leaders and analyses of AI tech companies.


Google AI related research has three major collections: Data Infrastructure and Analysis, Google Brain Team and Google AI Residency.

Three areas with most number of AI publications at Google:

  1. Machine Intelligence
  2. Machine Perception
  3. Natural Language Processing

Interview process

Google’s technical interview process is a standard technical interview process. It consists of Phone screen/s followed by onsite interviews. For technical interviews they have their interview guide: here.

TensorFlow
Important Reading(About Google AI)
  1. TensorFlow: A system for Large Scale Machine learning.
  2. Tools that Google uses both Hardware and Software: AI Tools
  3. Unofficial Google Data Science Blog
AI/Data Science Related Interview Questions
  • What is the derivative of 1/x?
  • Draw the curve log(x+10)
  • How to design a customer satisfaction survey?
  • Tossing a coin ten times resulted in 8 heads and 2 tails. How would you analyze whether a coin is fair? What is the p-value?
  • You have 10 coins. You toss each coin 10 times (100 tosses in total) and observe results. Would you modify your approach to the the way you test the fairness of coins?
  • Explain a probability distribution that is not normal and how to apply that?
  • Why use feature selection? If two predictors are highly correlated, what is the effect on the coefficients in the logistic regression? What are the confidence intervals of the coefficients?
  • K- mean and Gaussian mixture model: what is the difference between K-means and EM?
  • When using Gaussian mixture model, how do you know it is applicable? (Normal distribution)
  • If the labels are known in the clustering project, how to how to evaluate the performance of the model?
  • You have a google app and you make a change. How do you test if a metric has increased or not?
  • Describe the process of data analysis?
  • Why not logistic regression, why GBM?
  • Derive the equations for GMM.
  • How would you measure how much users liked videos?
  • Simulate a bivariate normal
  • Derive variance of a distribution
  • How many people apply to Google per year?
  • How do you build estimators for medians?
  • If each of the two coefficient estimates in a regression model is statistically significant, do you expect the test of both together is still significant?

Source: Glassdoor

Reflecting on the Questions

Google is known for its dense interviews. There is a mix of questions from practical perspective, general ML perspective as well as a theoretical perspective. A well read candidate with a little bit of luck can surely make it into one of the most prestigious AI companies in the world.


For a more consumable list of questions: 20 Google AI Interview Questions


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