Just landed that all-important data analytics job interview? Well done! It’ll have taken some hard work to get to this point. But no doubt, you’re now wondering what sorts of questions you can expect during your interview. Along with the usual data analytics interview questions, today’s job applicants often find it necessary to brush up on their machine learning interview questions and answers.
Since machine learning is such a vast field, it’s vital to focus on the right questions when prepping. That’s why we’ve compiled a list of the most common machine learning interview questions you’re likely to encounter. In this article, we will cover:
- What should I expect from a machine learning interview?
- Types of machine learning interview questions for data analysts
- Machine learning interview questions: general knowledge
- Machine learning interview questions: algorithms and theory
- Machine learning interview questions: programming skills
- Machine learning interview questions: company and industry-specific questions
- How to ace your machine learning interview
Ready to get the lowdown on everything machine learning-related? Then let’s dive in!
1. What should I expect from a machine learning interview?
First up, before we get to the detailed machine learning interview questions and answers, what format can you expect for your interview?
The main difference between conventional job interviews and those for machine learning and data analytics is that the latter typically involve some kind of practical task. Data analytics is very hands-on, so seeing how you perform under pressure on a practical task will give them a better sense of your analytical thinking and problem-solving approach, while also offering insight into your ability to apply machine-learning concepts to real-world data.
Common tasks include:
As the name suggests, a whiteboard test is an exam where interviewees must solve a problem on a whiteboard or by using pen and paper. Whiteboard tests typically require writing out some code or producing diagrams and workflows that outline how you would solve a given problem. Although you won’t necessarily be expected to create a perfect answer, the test will assess your understanding of the topic and measure your ability to work under pressure.
Related watching: What is a whiteboard challenge? This is in the context of UX design, but the basic principles apply for a machine learning interview!
Live coding task
Many interviews now dispense with the whiteboard test and dive right in with real-world coding! In this case, you will be set a task and placed in front of a computer to solve it. Live coding tasks typically involve solving a problem in real-time using a programming language like Python or R.
It’s likely that the test will also be timed, and you’ll be given a specific objective, such as implementing a sorting algorithm. Even if you don’t complete the task in the allotted time, you’ll be expected to produce clean, well-organized code that demonstrates the appropriate thinking.
For many interviewees, a take-home task is preferable, as it removes some of the exam pressure! Take-home tasks may involve writing a sample algorithm or completing another coding challenge. While they are usually less high-pressure than time-limited tasks on-site at the interview, the downside is that you will usually have to produce a more polished product.
You’ll also have to demonstrate your thinking by giving a presentation or talking the interviewers through your process when you meet them face-to-face.
Next, let’s look at the different machine learning interview questions you might encounter.
2. Types of machine learning interview questions for data analysts
While nobody can predict the exact questions you’ll be asked, we can broadly group common machine learning interview questions into four categories:
- General knowledge: These questions will assess your basic knowledge of machine learning in terms of the current trends and what’s happening in the industry. While they don’t involve practical tasks, you can think of them as a sense check to ensure that you have some basic knowledge and interest in the topic.
- Algorithms/theory: Once interviewers have established your baseline knowledge, they’ll typically want to probe your academic know-how. This might involve explaining the difference between popular machine learning algorithms and assessing when to use them and how to apply theoretical thinking to real-world data.
- Programming skills: Next up, potential employers will want to learn about your practical approach to programming. Questions relating to programming might be about how you use different coding languages and will likely accompany the task portion of your interview.
- Company and industry-specific questions: Finally, interviewers will want to know that you’re as familiar as possible with their company and the wider industry. These questions may not be super technical but are still important. They will demonstrate your understanding of the company’s problems and how you might use machine learning to solve them.
Now, let’s finally get to the machine learning interview questions and answers themselves.
3. Machine learning interview questions: general knowledge
These questions aim to assess your general interest in machine learning and the subject more broadly. Some common ones might include:
Q1. Do you have any machine learning experience?
Answer: If you’re applying specifically for a machine learning role, the answer to this question should be yes. It will also be clear from your resume or portfolio. However, be warned that the question might also crop up in a more junior data analytics interview.
Simply be honest and know that it is not a trick question! Perhaps you’ve worked in some sort of machine learning research role. If so, share your experience, what you learned, and your responsibilities. If you have no experience, it’s okay to be open about that. Put a positive spin on the answer by expressing your enthusiasm for the topic and sharing insights to demonstrate your genuine interest in the field, such as which aspects of machine learning you’d most like to learn and why.
Q2. What machine learning books or papers have you read recently?
Answer: This question aims to ensure you are interested enough in the topic to explore it in your free time. We can’t answer it for you, but you should familiarize yourself with some machine learning literature. For instance:
- Machine Learning: A Probabilistic Perspective, book by Kevin Murphy
- Deep Learning for Natural Language Processing: Advantages and Challenges, a short paper by Hang Li
- Any recent paper by the renowned computer scientist Yoshua Bengio
- Neural Networks and Deep Learning, a free online book by Michael Nielsen
There’s lots more out there, of course. You can also check out some of these data analytics books for beginners, many of which contain vital machine learning theory.
Q3. What impact do you think quantum computing will have on machine learning in the future?
Answer: Quantum computing is making strides, which will have a huge impact on machine learning. This question aims to ensure you understand the importance of these technological advancements. You might want to answer with something like:
“It’s hard to overstate the relationship between quantum computing and the evolution of machine learning. As we know, machine learning has been around for decades, but it’s only recently that we’ve had the computing power for machine learning algorithms to process the vast amounts of data needed to be useful.
Quantum computers will be able to boost this computing power many times over. Algorithms will be capable of working through this data much faster than classical computers, learning more quickly, making ever more accurate predictions, and shaping the future in ways we can’t yet comprehend.”
Q4. What are your favorite applications of machine learning models?
Answer: This question aims to tease out your knowledge of machine learning applications in the modern world. Your answer should demonstrate that you know how companies are using machine learning. If appropriate, you can also go further by showing that you know which applications might benefit the company you are applying to work for the most. For instance:
“Machine learning has so many applications, it’s hard to say which is my favorite. But the capabilities of modern image recognition algorithms are frankly incredible, not to the advances in AlphaFold, which has recently predicted the structure of nearly all known proteins.
Text and speech recognition are also making strides in personal digital assistants. But I think my favorite application of machine learning in business has to be predicting consumer behavior. This has huge potential to shape business strategies and drive forward the bottom line, just as it would for your company if I had the opportunity to work for you.”
Related watching: How seriously is the field of data science taking the issue of bias in machine learning?
Q5. Where do you source your datasets?
Answer: It’s one thing being a machine learning enthusiast, but where do you get your data from? As such, your answer might be:
“While this depends on what problem I’m trying to solve, in my experience, great places to look for datasets include repositories like Kaggle, Data.gov, and UCI Machine Learning Repository, as well as individual organizations that make their data available for public use. For more specific requirements, I’d say that creating a custom data pipeline and data collection strategy for first-party data may be the most appropriate approach.”
Q6. Do you know how Tesla creates training data for its self-driving cars?
Answer: A question like this is probing to see if you know how companies are training their algorithms. In this case, your answer might be something like this:
“As we know, all machine learning algorithms require labeled training data. I know from my research that Tesla uses two main approaches to tackle this. Firstly, they use manual image labeling, both in 2D and 4D vector space reconstructions. Most data needs to be labeled this way.
However, they’ve also recently developed some complex auto-labeling systems that they are using to extract key features from visual and GPS data (amongst other data formats) before generating labels. For broader context, Google also uses reCaptcha to help train data for their self-driving cars.”
4. Machine learning interview questions: algorithms and theory
Next, we’ll move on to machine learning interview questions that aim to test your knowledge of machine learning algorithms and theory. Since these all have definitive answers, keep your responses accurate but punchy, using technical terms when necessary but sticking to plain English when possible. If you don’t answer in enough detail, the interviewer will usually prompt you to expand your answer.
Q7. What is the difference between supervised learning and unsupervised learning?
Answer: “Supervised learning is a type of machine learning algorithm that uses labeled datasets. It trains models to predict the output of new data. Typically, the labels for these data are provided by a human supervisor. The data are then used to teach the model what the correct output should be for each input.
Meanwhile, unsupervised learning is a type of machine learning algorithm that does not use any labels (or supervision) to train the model. Instead, it uses only unlabelled input data. The model then learns the underlying structure of the data to make predictions about new inputs.”
Q8. Can you explain the difference between KNN and k-means clustering?
Answer: “KNN (or K Nearest Neighbors) is a non-parametric algorithm used for classification and regression. KNN is a supervised learning model. KNN works by finding the distance between a new point and all labeled training points. The training point with the smallest distance is then used to predict the label of the new input. This point is then assigned to the class that has the most training points within the vicinity.
Meanwhile, k-means clustering is an unsupervised learning model that groups data points together based on their similarity. In this case, the algorithm measures similarity by the distance between data points, rather than by using labeled training data. Finally, the data points are clustered together such that the points within a cluster are more similar to each other than those in other clusters.
While these two algorithms are similar, the key difference is that KNN uses labeled data and k-means clustering does not.”
Q9. What is a decision tree?
Answer: “In machine learning, a decision tree is an algorithm that splits datasets into smaller and smaller subsets. It is commonly used to help choose between alternative options or to determine the optimal path. The decision tree has both decision nodes and leaf nodes. The decision nodes are where the tree splits and the leaf nodes are the final decisions. Decision trees are particularly useful for handling non-linear data sets.” Learn more about decision trees here.
Q10. What is a random forest?
Answer: When answering a question like this (or the previous questions) the obvious priority is explaining the concept clearly. However, when appropriate, you can also expand on the different terminology. Doing so is a great way of showing that you know how to explain things in clear, non-technical terms. For instance:
“A random forest is a type of ensemble model composed of numerous decision trees. An ensemble model like this runs several related but different analytical models and then synthesizes the results. In this case, the individual decision trees in the forest are trained on different subsets of the data. The subsequent predictions of individual trees are then combined to produce the final prediction. An advantage of using a random forest over a single decision tree is that it is much less likely to overfit the data.” Learn more about random forests here.
Q11. Can you explain logistic regression?
Answer: “Logistic regression is a type of statistical analysis used to predict the probability of an outcome occurring. The outcome is always binary, meaning it can only be one of two things, such as yes or no, success or failure, etc. Logistic regression models use a formula to calculate the probability that the outcome will occur based on certain input variables.
The model then uses this probability to predict whether the outcome will happen or not. As a binary model, the output is either a 0 or 1. Values above 0.5 are considered 1, and values below are considered 0.” Learn more about logistic regression here.
Q12. What is Bayes Theorem and how does it apply to machine learning?
Answer: “Bayes theorem is a way of calculating the probability of something happening, given that something else has already occurred. In short, it provides the conditional probability of an event based on the values of specific, related, known probabilities.
For instance, if you know that there is an 85% chance of rain in the morning, and you also know that when it rains, there is a 95% chance that the sun will not be out, you can use Bayes Theorem to calculate the probability that the sun will not be out tomorrow morning. Within machine learning, a classification algorithm known as a Naïve Bayes classifier (which is a simplified version of the Theorem) can be used to classify data into various classes, quickly and with high accuracy.
That said, Naïve Bayes classifiers tend to make the strong assumption that the features in a dataset are independent of each other, which is not usually true in real-world datasets.”
Q13. What is the F1 score?
Answer: The ability to measure the success of a machine learning algorithm is as important as it is for any programming task. As such, you will likely get questions about the different evaluation metrics you can use (including recall, precision, and the F1 score). Before your interview, you should aim to read up on all success metrics, but in this case, your answer might be:
“The F1 score measures how well a machine learning classifier (or class labeling algorithm) performs. It takes into account both the precision and recall of the classifier. The score is then calculated by taking the harmonic mean of the precision and recall. The precision is the number of true positives divided by the sum of the true positives and the false positives. The recall is the number of true positives divided by the sum of the true positives and the false negatives.”
5. Machine learning interview questions: programming skills
Once you’ve explored your general knowledge and theory, machine learning interview questions will often assess your practical programming skills. Regularly accompanied by a task, these questions help the employer determine whether you have the skills to carry out the required job tasks.
Q14. What big data tools have you used?
Answer: While all data analysts use data management tools at some point, make sure you consider the context here. Specifically, what tools have you used—or are at least familiar with—that are common in a machine learning setting? Common tools used for machine learning include big data tools, like Apache Hadoop, Apache Spark, and NoSQL databases. These tools, used for distributed computing, are necessary for managing big data and real-time web applications. Apache Spark is arguably the most popular right now.
Spark is a powerful open-source processing engine built for speed, ease of use, and sophisticated analytics. It’s used for various machine learning tasks, such as classification, regression, clustering, and dimensionality reduction. If you’ve never used any of these tools, be honest. But try to familiarize yourself with them before the interview, so at least you don’t have to give the interviewer a blank expression if they ask you!
Related reading: The top machine learning tools
Q15. Which programming language would you favor for machine learning?
Answer: This could either be a trick question (answer: depends on the task!) or a genuine query to see which programming language you’re most comfortable using. Either way, a frank assessment of the options might be your best bet. You could say something like:
“Both Python and R have advantages and disadvantages when used on machine learning tasks. Some people may prefer Python because it is a more general-purpose programming language and has countless libraries that make these tasks easier. However, others prefer R for its power in statistical computing and because it is a lower-level language. It’s also more widely used by statisticians and data scientists.
My personal preference, however, is Python, although Java is also very robust and has better error-checking than either of the other two. Like Python, Java also has a large and active community, which makes it easy to find help and resources.”
Q16. How would you compare CSVs with XML and JSON?
Answer: CSV, XML, and JSON are all common file formats used by data scientists, analysts, and machine learning engineers. Each has different features and this question is testing your knowledge of these. Your answer might be:
“Generally speaking, CSV is much simpler than XML, both in terms of its syntax and its structure, using commas to separate data into columns. Programmatically, this makes CSV files far easier to work with. It’s also worth noting that they’re usually smaller than XML files, which makes CSVs easier to download and parse.
However, XML can be used to preserve data formatting in ways that CSVs cannot. XML also supports hierarchical data. Meanwhile, JSON combines the best of both CSV and XML: it remains compact like CSV (typically JSON files are only about twice as large as similar CSVs) while also supporting hierarchical data like XML. On the downside, JSON’s data structure is not as robust as XML.”
Q17. What would be your approach to developing a data pipeline?
Answer: All data analysts and machine learning engineers need to produce data pipelines. In this question, you should talk the interviewer through the process, including which tools you might use. These might include things like Apache NiFi, Apache Kafka, and Apache Flume.
You should also consider the data sources you might be working with, identify the data transformations that need to be performed, how you would design the architecture of the pipeline, and how you would test and deploy it. Cover each of these bases and you shouldn’t go too far wrong.
Q18. Which data visualization libraries and tools do you use most?
Answer: This is another question that will depend on your preferences. Python, in particular, has a wide array of fantastic, open-source data viz libraries available on the Python Package Index. Check the job description before the interview, though, to see if they mention any specific tools that they use. Otherwise, play around with Python libraries like Matplotlib or Seaborn to get a feel for them.
Meanwhile, if you’re an R user, ggplot2 is popular. Finally, there are also lots of proprietary data visualization software out there. This includes Tableau, Power BI, and Qlikview.
Q19. How would you manage the issue of missing data in a dataset?
Answer: The answer to this question might benefit from an explanation of the tools and commands you’d use to fix data corruption issues. Broadly speaking, though, you might want to start by talking the interviewer through the different options:
“There are a few ways to manage missing data, depending on the amount and type. If only a small amount of data is missing, you can simply delete the relevant rows or columns. Of course, this is only an option if the amount of missing data is small and if the data is unimportant for analysis.
If a large amount of data is missing—which may be more common in vast machine learning datasets—another option is to impute the missing data by substituting the missing values with estimated ones. I might do this by using the mean or median of the data, or by applying a regression model to predict the missing values.
If the data is missing completely at random, I might use multiple imputations to estimate the missing values. This is a more sophisticated method that uses statistical techniques. There is also the option of creating an ‘unknown’ category for missing values.”
6. Machine learning interview questions: Company and industry-specific questions
As the title of this section suggests, questions in this category will be specific to the company and industry you’re applying for a job with. This makes predicting these types of questions more difficult, but here are some broad examples to give you a taste of what to expect.
Q20. How would you use machine learning to support our business?
Answer: Broadly speaking, this is a common question you may face, although it may be more specific in context. Your general approach to answering this, though, should focus on the company’s objectives, the problems it faces, and what kind of data it has access to. For instance, maybe you are applying to a media company that needs a new personalized recommendation engine for its subscribers. What data would you need to create this, and how would you go about designing a suitable algorithm?
Alternatively, perhaps you’re applying to work for a financial institution. How might you use machine learning to detect and prevent fraud? Which existing customer data could you use? While the specifics of the question will vary depending on the context, this question will always involve listening carefully and researching the company before you get to the interview.
Q21. What, in your opinion, is the most valuable data in our sector?
Answer: Following the previous question, this dives a little deeper into the kinds of data the company collects and how they are currently using it. While they’ll unlikely expect you to know everything about their inner workings, you should research their business model and industry landscape.
This type of question is a top opportunity for starting a conversation, asking thoughtful questions of your own about their objectives, the data they have, and the data they are missing. Doing so will show that you are thinking carefully about their needs, while offering them some insights into the value you might bring to the business.
Q22. How would you improve our current data collection processes?
Answer: This is a machine learning interview question that requires a careful answer. The question aims to determine how well you grasp their business model and current machine learning and data collection methodologies. However, be careful not to step on their toes:
“In my opinion, there are a few ways you could improve your data collection processes. Firstly, I’d recommend collecting customer data more frequently, allowing you to track their behavior over time. This will improve predictions and drive more accurate business decisions. I’d also recommend collecting data from a wider range of sources. This would offer a more complete picture of what is happening in the industry. It’d also allow you to feed your machine learning models larger datasets, which will improve accuracy (presuming the high quality of the data collected).
Finally, I think there’s always scope to use more sophisticated data collection methods. For instance, I notice you rely heavily on digital data pipelines, which is great. But with the right resources, you could supplement this data from focus groups, in-depth interviews, and ethnographic research.”
7. How to ace your machine learning interview
While it’s impossible to know what you’ll be asked during your interview, there are a few things you can do to ensure you ace it, no matter what curveballs the interviewer throws!
1. Polish up your programming skills
As discussed earlier, you’ll likely face a programming task. Ensure you’re up to speed with your preferred programming language—whether Python, R, Java, or another—and get plenty of practice before you get to the interview itself. Practice regularly by writing code and solving challenges (such as Kaggle competitions) or studying code written by experienced developers. If you’re not confident with your programming skills, you could attend a bootcamp or participate in online forums such as Stack Overflow.
2. Get up to speed on your machine learning theory
Nobody’s expecting you to know everything about machine learning—it’s a vast field. But you should get to know the various algorithms common in machine learning, from support vector machines, to decision trees, and artificial neural networks. You should also learn when to use each.
This might involve being able to explain how you would develop a machine learning model for various tasks that the company might need it for. Don’t fret about perfecting this, but it’ll help to go in armed with some stock answers to get you started!
3. Read up on the industry (and the company)
Make sure you’re armed with all the knowledge you can get your hands on about the company and the industry they work in. This can help you narrow down your machine learning interview prep to areas in which you know they specialize, or on tools that you know they use.
4. Understand the data types used in machine learning, and how to pre-process them
It’s one thing to know the data types that machine learning uses (from images and videos, to sound, as well as numerical and text data). But you’ll need to get under the hood of these, too, understanding which algorithms each is best suited to and what their common use cases are. Try to exhibit an awareness of how to pre-process these data types, too. Some of this is basic data analytics processing, but it can become more complex for things like video data.
5. Be familiar with the issues that arise during the training and deployment of machine learning models.
In machine learning, as with any data task, things can go wrong. Make sure you learn what problems tend to arise with machine learning models. For example, three common ones include data leakage, overfitting, and concept drift. A quick explanation for you… Data leakage occurs when training data used to build a model is not representative of the data the model will be used in the real world (leading to poor performance on new data).
Meanwhile, overfitting is when a model is too complex and captures too much detail from the training data, leading to poor performance on new data. Finally, concept drift occurs when the real-world distribution of the data changes over time, leading to the model no longer being accurate. This isn’t an exhaustive list, but it’s good to be aware of the machine learning pitfalls!
7. Stay upbeat and honest!
It’s only normal to feel a little apprehensive about your interview. But remember: there’s only so much knowledge one person can hold in their head. Stay positive, and be upfront about where your knowledge gaps lie. If you get stuck, just be honest and upbeat and reassure the interviewer that you are keen to fill those gaps as soon as they employ you. And good luck!
As we’ve seen in this post, machine learning interview questions can be extremely wide-ranging, covering plenty of bases. From general knowledge to algorithms and theory, programming skills, and insider industry know-how, you’ll certainly have to do your homework.
However, we hope this post has also shown that, so long as you do the right preparation for an interview, you can enter confidently, with a positive attitude, and will ace it.
To learn more about a possible career as a machine learning engineer, data scientist, or data analyst, check out this free 5-day, data analytics short course. You may also be interested in the following articles: