Hey everyone, it's Jay here, and today I want to go over the seven different types of machine learning interview questions that you'll see on the interview and then example questions and solutions for each. All right, let's dive into it. So if you're probably here, 

you're probably preparing for a data science interview or a machine learning engineering interview or something in the data or AI field. While machine learning questions might seem straightforward, there's actually a lot of different types of machine learning questions depending on what role you're going for and how senior you are as well. 

Which brings me to the first type of machine learning question, which is kind of the basic concept ML question. So these kind of questions are basically what you're going to see if you're an intern, your entry level, or maybe you're not completely in the data science ML 

domain, and they're just going to ask you if you know anything about machine learning. And this is just to test your knowledge about what you know about machine learning and how much you might know about it. So for example, they might ask you just like, what does bias mean? 

What does variance mean? They might ask you to define different kinds of concepts of machine learning. So they might ask you to explain what a neural network is to a fifth grader to see if you can actually explain a difficult concept. As I said before, you'll you see 

these types of interview questions if you're just kind of new into the field or if you're in a field that's kind of related to machine learning. So let's say that you're a product manager that's joining like an ML startup, or maybe you're even in sales and you're selling 

like an ML B2B SaaS product, you just have to know kind of these general concepts that they probably want to test you on so that they know that you can learn it on the job, kind of get more in depth on it or did just a little bit of

preparation for your interview now if you're actually interviewing for a data science or machine learning job, that's where you're going to see the next type of interview question, which is around algorithms and theory, so these questions are going to test your conceptual knowledge of machine learning and go a

lot more in depth than kind of the basic ones. For example, the three common ways that we see these algorithm questions come up are when you one compare different algorithms with each other and test your knowledge of knowing the differences there. Two, identifying the similarities between different

algorithms. Three, just conceptually being able to explain how an algorithm works. So for example, one of these could be when should you be using random forest regression versus linear regression? And a lot of the times this will be associated with a case study as well. So

an example scenario here would be, uh, let's say that you're interviewing at Airbnb and you want to build a model to predict booking prices on Airbnb. The interviewer might ask you between linear regression and random forest regression, which model would perform better and why? So now you

actually have to use context around the business to actually apply the differences between these two different kinds of models. Another kind of question that might be in this domain that's a little bit more straightforward is talking about just how would you prevent overfitting in a deep learning model?

again for this kind of question we're not just asking directly you know what is a deep learning model right we're already assuming that you know what a deep learning model is and then we're asking for the nuances in between it and if you don't know the solution to this

it's pretty clear that you can reduce overfitting by training the network on more examples or reduce overfitting by changing the complexity of the network lastly a really common interview question in this kind of scenario is around explaining the technical concepts to a non-technical audience now we kind

of mentioned this the last version but a lot of the times it's going to be for a more business scenario in these kinds of questions for example one really common question we see is something like your manager asks you to build a model with a neural network to solve a business

problem how would you justify the complexity of building this neural network so here we actually have to justify this to non-technical stakeholders right because there's going to be a lot of costs involved as well and I've actually already tackled this question so you can check it out in the

link in the description below where I do a mock interview with Sundeep kind of tackling how he would answer this kind of question all right the third type of interview question we see in machine learning is around the modeling case study now case studies are really common

for all types of data science engineering interviews but for this one around the modeling case study we're generally given a problem scenario about how we would build a model from the very beginning all the way to the end and that means starting from basically gathering the data to deployment of the

A model in production, a really classic example would be from Uber. Here, it'd be like how would you build the model to predict if a driver on Uber will accept a ride request? Generally, we have a pretty simple framework that we can apply to every single one of these

interview questions. And the reason behind that is because the building of the model does follow a step-by-step process. First, we have to get the data and we have to explore it and do a lot of pre-processing. Then we do our feature selection and engineering. Then we go

through model selection, and then we do a lot of cross validation and feature performance. Then we do a little bit of evaluation metrics and then we talk about how we would roll it out, test, and iterate on the model performance as well. And if you want to see a step-by-step

answer done by Chima, you can check it out in my other mock interview video with him on how we tackle this Uber rider requests problem. The last thing I want to say on this is that a lot of the times for interviews, they'll actually take a specific subset of this modeling

case study problem and not actually ask you about the entire life cycle of developing the model, but just tackle one simple part of it. So you might be given context around specifically how you might evaluate and iterate on a model that's already been built in production.

And that's just like the last half of the original model case study. It's definitely important to know the ins and outs of anything in the modeling case study, which leads to the fourth type of ML problem, which is the applied modeling case study, applied modeling questions.

take machine learning concepts and ask how they could be applied to fix a certain problem. These questions are a little nuanced, but they're great for more experienced machine learning engineers and data scientists. They're very similar to the case studies you do, but you have to

require a lot of context and you have to probe the interviewer for a lot of different kinds of questions. For example, this is a question that's asked at a firm like Square, AKA Block now. So we have a binary classification model that classifies whether an application should

be qualified to get a loan or not. However, we do not have access to the feature weights. How would you provide each user with a rejection reason? So as you can see, it's not really like a modeling case study, like we don't actually need to build a model from the ground up and

then show how we're actually going to deploy it. Right? Instead, we're taking an example scenario that might occur around machine learning, and we're trying to then kind of solve the problem that's occurring here. Another example question asked by Zillow is something like, let's

say we have a housing data set and 20% of our listings do not have square footage information. How would you deal with this missing data? Again, a very classic modeling question, right? We're just dealing with the concept of data imputation in our data set, right?

And now we have to find ways that we can actually probe the interviewer to solve this. Right? In some ways, we might want to take an average of a certain variable. In other ways, we might just want to eliminate all that data. We don't know the specifics of which way we should go.

down unless we ask the interviewer for more context. All right, the fifth type of ML interview question is around the machine learning system design interview question. Now, this one is probably the hardest one and for more senior machine learning engineers, a lot of the popularity around software engineering

now has kind of gravitated towards system design because of the fact that besides just testing you on typical leetcode or data structures and algorithms questions, a lot of the companies are now kind of testing on system design to see how you function when you're not just

given, you know, immediate tasks to just code but also tasked with doing things that are a little bit more impressive, such as designing infrastructure and systems. And that goes for machine learning as well. For machine learning system design, there's also a general framework we can use for tackling these

kinds of questions. First, we want to set the problem statement. Second, we want to architect the high-level infrastructure. Three, we want to explain how data moves from one stage to the next. Four, we want to then understand how to measure the performance of machine

learning models. And then five, deal with the common problems around scale or liability and deployment. Now, if you're saying, hey Jay, this actually sounds a lot like the modeling case study, what's the actual difference here? Then, yeah, I agree with you. The main thing is that a

lot of this deals with the more technical nature of scaling and performance here. So, for example, the questions that you might get asked here is, how would you build a machine learning system to generate Spotify's Discover Weekly playlist? Now, you could tackle this the same way that you're

talking about the regular model machine learning case studies, but a more experienced engineer would understand the system design aspect of this. This is the important part of this question because Spotify's Discover Weekly playlist is a huge playlist that gets sent to millions and millions of users, so it's going to

be more about the actual performance of the model than the actual machine learning algorithm part of it. As well, intuitively we know also from this question that basically when we're tackling with Discovery Weekly and something that is more about music recommendation, it's not so much as

a binary yes I love this, no I hate this kind of formula, but more so about getting really, really good performance in towards tackling this kind of question. And you can check this question out on Interview Query. All right, the sixth type of machine learning question that we're going to tackle now is the

recommendation and search types of machine learning questions. Now these come up so often in ML interview questions that I feel like I have to address it specifically because of the fact that most problems that a lot of big tech companies face are around recommendations and search. A lot of

these big tech companies are platforms, and because they're platforms, that means their biggest issue is recommending, obviously, users to do more things or helping them find better search results. So think about Google, it's search, right? Amazon, it's search and recommendation, finding you similar products. Facebook,

it's a lot of recommendation. The Instagram feed, right? How do we design that? So an example case study question would be, you work at Facebook and your task with building a restaurant recommendation engine. What data would you use and how would you build it? Or how would you build a video

recommendation system for YouTube. The main goal here is to maximize user engagement. So again, this is a combination of both the system design level questions and the modeling case study questions where we have to tackle the nuances in between. There are a lot of these questions that are also going to be

asked if you have a lot of previous experience in recommendation and search, and if you're just an entry-level machine learning person or a data scientist, they're not going to ask you or expect you to know all this in-depth knowledge about designing systems that

you've never done before, right? And so here, the correct solution or the appropriate litmus test of a solution would be just kind of talking about different kinds of recommendation algorithms and search algorithms that you know, but also saying that okay, well there's, if the interviewer wants to go

deeper into the system design, probably letting them know that you don't know that. But for the most part, most interviewers know how to level set you. Whereas if you're an experienced machine learning engineer with like 2 to 5 years of experience or maybe even 10 plus

years of experience, they don't expect you to know exactly how to design these systems from scratch, or at least talk about your experience about how you design a lot of these systems. All right, the last type of interview question out there is the one that I hate the most, but it's

the machine learning coding interview question, and a lot of these are around basically coding ML algorithms from scratch or implementing them in a way so that the interviewer can see that you understand the nuances behind a lot of the algorithms. For example, one really common question that I see is, build

random forests from scratch now a lot of the time this might actually show up in the take-home exercise but sometimes it actually does show up on the interview where basically the interviewer will give you some template code and you'll be designed and actually building random forests from scratch and not using any of

these libraries like scikit-learn or anything else I personally hate these questions because I don't think they're actually indicative of you knowing a lot of the actual ML theory or doing well on the job here it's more about kind of seeing how well you can perform on a lot

of these different types of algorithm kind of LeetCode style questions a lot of the time startups will ask these questions because they don't have a very set process or they'll ask these questions to really find out if you're a 10x engineer who not only knows the

theory behind every machine learning algorithm but can also code it from scratch in like 20 minutes but just as a caveat they do come up so I think it's good for you to practice and we've seen companies ask this question before in the past a more nuanced question that's a

little bit better than coding of algorithms and scratch is something like this given a sorted list of integers with no duplicates write an efficient function called nearest entries that takes in integers n and k and finds the element in the list that is closest to n and returns that element along with the

k next and k previous elements of the list so as you can see this sounds like a typical algorithms data structures interview question right that's just generally pretty hard but in actuality it's also testing you on the nuance of the k-nearest neighbors algorithm itself

It's trying to see if you can actually do this kind of algorithm that's within K neighbors, and so yes, you'll see these kinds of questions out there. They will get asked in machine learning engineering interviews, unlikely to be asked in more data science interviews or

more analytics types interviews, but definitely like the more technical you are, let's say you're going to AI research science stuff like that where you're building a lot of these models from scratch, the more you're going to be getting these kinds of interview questions for them to kind of test your

knowledge. All right, so those are the seven different types of machine learning interview questions, and if you want to ace your next machine learning interview, just knowing the answers to the questions isn't really enough. You really do have to practice to get better and better at it, so I encourage you to

check out more of the resources on Interview Query. Interview Query is the fastest route to actually nailing and acing your machine learning and data science interview, but don't just take our word for it. Here's what some of our members have to say. Steel says, "I joined

Interview Query a year and a half ago and really loved what it provided. The practice questions and courses really helped me land my job as a senior data scientist. I rejoined again in preparation for my machine learning engineering interviews, and I found it

very helpful for that as well." And see what another member wrote. Manasi wrote, "Interview Query provides a well-structured approach to prepare for data science and machine learning interview questions. Practice questions and well-conceptualized solutions help me feel confident to get a job at LinkedIn as a

I'm a data scientist and I hope all of you guys accomplish your machine learning goals and your dream job. If you're intrigued to see if the interview site is right for you, check out the link in the description below to learn more and check out more of the practice questions.

on our site. We have a lot of the questions that we went over in this video on that site along with example solutions and other people's discussions and comments from other members on interview query. Regardless, thank you for watching. Please like and subscribe and

7 Types of Machine Learning Interview Questions!

Jay Feng

Tests foundational knowledge of machine learning concepts.

Examples: defining bias, variance, or explaining a neural network simply.

Target audience: interns, entry-level roles, or those peripherally related to ML.

Relevant for roles like product managers in ML startups or sales of ML products.

Focuses on in-depth conceptual knowledge of ML algorithms.

Examples: comparing/contrasting algorithms (e.g., linear vs. random forest regression), explaining algorithm functionality.

Often involves case studies applying algorithms to real-world scenarios (e.g., Airbnb pricing prediction).

Example: preventing overfitting in deep learning models.

Explaining technical concepts to non-technical audiences (business scenarios).

Covers the entire model-building process, from data gathering to deployment.

Example: building a model to predict Uber driver ride acceptance.

Framework: data acquisition, preprocessing, feature engineering, model selection, cross-validation, evaluation, deployment, iteration.

May focus on a subset of the process, like model evaluation and iteration.

Applies ML concepts to solve specific problems; requires contextual understanding and probing.

Example (Square): providing rejection reasons for loan applications without feature weights.

Example (Zillow): handling missing square footage data in a housing dataset.

Focuses on designing ML infrastructure and systems; more challenging, for senior engineers.

Framework: problem statement, high-level architecture, data flow, performance measurement, scaling/liability/deployment.

Differs from modeling case studies by emphasizing technical aspects of scaling and performance.

Example: designing a system for Spotify's Discover Weekly playlist.

Common in big tech due to platform nature; focuses on recommendation and search algorithms.

Examples: building a restaurant recommendation engine for Facebook or a video recommendation system for YouTube.

Goal: maximizing user engagement; combines system design and modeling aspects.

Difficulty varies based on experience level; entry-level candidates may focus on algorithms, while senior engineers discuss system design.

Coding ML algorithms from scratch (e.g., random forests); often appears in take-home exercises or interviews.

Assesses coding proficiency and algorithm understanding; less indicative of on-the-job performance.

Example: finding the nearest element in a sorted list and its k neighbors.

More common in roles requiring building models from scratch (AI research, etc.).