Machine learning questions are asked in a wide range of Google interviews. Some of the roles that can expect machine learning questions include:
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.
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 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:
Below you’ll find a brief overview of these concepts and samples of Google Machine Learning interview 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:
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.
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.
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.
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.
With this closest key Python question, you’re given a dictionary with keys of letters and values of a list of letters.
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
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.
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.
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.
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?
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.
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.
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.
This would include topics monitoring and optimizing machine learning solutions, data preparation and processing, design ML pipelines and machine learning frameworks.
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.
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.
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.
Check out our Machine Learning Algorithms course, where you’ll find lessons on modeling and machine learning, and machine learning system design:
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.