Google Machine Learning Interview Questions

Google Machine Learning Interview Questions


Machine learning questions are asked in a wide range of Google interviews. Some of the roles that can expect machine learning questions include:

  • Data scientists
  • Data engineers
  • Research scientists
  • Machine learning engineers
  • AI engineers
  • Machine learning scientists

No matter the role, algorithms are the most commonly tested subject in Google machine learning interviews.

You can expect definitions-based questions and explanations of common algorithms, algorithmic coding questions in Python, as well as questions that ask you to write algorithms from scratch.

Beyond algorithms, though, Google machine learning interview questions tend to focus on ML system design and applied modeling. This guide covers everything you can expect in machine learning interviews at Google.

Machine Learning Roles at Google

Data scientists and data engineers at Google should be well-versed in machine learning techniques, and ML questions inevitably come up in these interviews. Yet, several Google roles are machine learning and AI specific. They include:

  • Machine learning scientists - Google hires a number of data scientists that specialize in machine learning. Machine learning scientist roles typically focus less on analytics and more on architecting and tuning ML models.
  • Machine learning engineers - Google is one of the largest employers of machine learning engineers in the U.S. These roles focus on designing and deploying ML models and pipelines.
  • Research scientists, ML - Google hires researchers to analyze and improve new and existing ML methods. A ML research scientist at Google, for example, might be responsible for developing a new library or optimizing an existing one.
  • AI engineers - AI engineers at Google develop and deploy new AI systems, with a focus on knowledge representation, searching techniques, and reasoning.

What Gets Asked in Google Machine Learning Interviews?

Machine learning algorithms are a key concept to study. In particular, you should have a strong grasp of supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning algorithms. Overall, the most frequently asked topics are:

  • Algorithms
  • Coding
  • Machine Learning System Design
  • Applied Modeling
  • General Machine Learning

Below you’ll find a brief overview of these concepts and samples of Google Machine Learning interview questions.

Google Machine Learning Algorithm Questions

Machine Learning algorithms are discussed in depth and include explanations and case studies involving a wide range of topics. Some algorithm concepts to study include:

  • Classification
  • Regression
  • Clustering
  • Dimension reduction
  • Control learning

Q1. What methods would you use to increase search recall without modifying the underlying logic of an algorithm?

This search algorithm recall question asks you to improve product search results. Remember that recall is the fraction of relevant documents successfully retrieved over the total amount of relevant documents.

Q2. How would you interpret coefficients of logistic regression for categorical and boolean variables?

Boolean variables are variables that have the value 0 or 1. Examples of these variables include gender, whether someone is employed or not, or whether something is gray or white.

The sign of the coefficient is important. If you have a positive sign on the coefficient, then that means, all else equal, the variable has a higher likelihood of having a positive influence on your outcome variable. Conversely, a negative sign implies an inverse relationship between the variable and the outcome you are interested in.

Q3. What are the assumptions of linear regression?

There are several assumptions of linear regression. These assumptions are baked into the dataset and how the model is built. Otherwise, if these assumptions are violated, we become privy to the phrase “garbage in, garbage out”.

The first assumption is that there is a linear relationship between the features and the response variable, otherwise known as the value you’re trying to predict. This is essentially baked into the definition of linear regression.

Google Machine Learning Python Questions

Expect several Python machine learning coding questions in a Google Machine Learning interview. In particular, these rounds will focus on data structures, databases and object-oriented programming.

Q1. Write a function closest_key to find the key with the input value closest to the beginning of the list.

With this closest key Python question, you’re given a dictionary with keys of letters and values of a list of letters.

Q2. Write a function to calculate the root mean squared error of a regression model.

With this root mean calculation question, the function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

Q3. Write a function find_bigrams to take a string and return a list of all bigrams.

Bigrams are two words that are placed next to each other. Two words versus one in feature engineering for an NLP model gives an interaction effect. To actually find the bigrams and parse them out of a string, we need to first split the input string.

ML System Design Questions for Google

General system design, as well as machine learning system design questions, will come up. These questions ask you to design architecture and define product features for complex systems.

Q1. How would you create a recommendation engine to help users looking for a new rental unit?

With this rental listing recommendation problem, you have data about users concerning their demographic information and interests, as well as houses and apartments to be recommended. Lastly, you have topic tags and metadata such as amenities, price, reviews, location, etc.

Q2. How would you design a classifier to predict the optimal moment for a commercial break during a video?

Before trying to answer this commercial break recommendation question and applying any technique, the question has to be framed in a meaningful statistical problem so that we can use machine learning. How do we measure whether an ad break is being shown at the ‘optimal’ moment?

Google Applied Modeling Questions

Applied modeling case questions ask about practical machine learning. These questions ask you to examine a problem, and describe how you would go about investigating and fixing it.

Q1. How would we know if we have enough data to create an accurate enough rider ETA model?

With this model evaluation question, assume you have 1 million rider trips in the city of Seattle. Is that enough data? This question assesses your ability to evaluate a model.

Q2. How would we give each rejected applicant a reason why they got rejected?

With this rejection reason question, suppose we have a binary classification model that classifies whether or not an applicant should be qualified to get a loan. Because we are a financial company we have to provide each rejected applicant with a reason why, but we don’t have access to the feature weights.

General Machine Learning Questions for Google

This would include topics monitoring and optimizing machine learning solutions, data preparation and processing, design ML pipelines and machine learning frameworks.

Q1. How would you explain the bias variance tradeoff in machine learning to a high school student?

With this bias variance tradeoff problem, you should define the terms and then talk about the tradeoff. Variance measures how well the trained model represents the training data. Bias measures how well the trained model generalizes to data outside the training set.

Q2. How would you justify the complexity of building a model with a neural network and explain the model’s predictions to a non-technical stakeholder?

This question asks you to justify a neural network model, and you’d likely need some more information to answer. Start by gathering information about the neural network, the dataset, the timeline and the business problem it is solving.

Q3. When would you use regularization vs cross-validation?

With this regularization vs validation problem, remember that cross validation is about choosing the “best” model, where “best” is defined in terms of test set performance. Regularization is about simplifying the model.

Learn More About Machine Learning Algorithms

Check out our Machine Learning Algorithms course, where you’ll find lessons on modeling and machine learning, and machine learning system design:

More Machine Learning Resources for Google

Start with our guide: Machine Learning Interview Questions, which features 30+ real interview questions asked by companies like Google. It provides an in-depth look at what types of questions get asked in machine learning interviews.