45+ machine learning interview questions to ask professionals

Machine learning (ML) is a rapidly growing and ever-changing industry. As the modern world grows more dependent on it, we are seeing many new roles appear within the field. 

It can transform your industry and will only become more relevant as the years go by.

If you’re looking to fill a machine learning role within your company, it can be challenging to filter potential candidates and find the right one. 

Our Machine Learning test can help you find those with the right experience – but what do you ask at the interview stage?

Here are more than 45 machine-learning interview questions to give you all the information needed to make the right choice.

• ✅ Make skills testing part of your recruiting process

10 introductory and knowledge-related machine-learning interview questions to start the interview

An interview for a role with machine learning can be complex. Begin the interview with these introductory and knowledge-related questions to learn about your candidate.

1. What interests you in our company?
2. What is your notice period?
3. How important is a flexible work arrangement to you?
4. Do you have any questions about the role?
5. Are you willing to relocate?
6. What is machine learning? How does it differ from data mining?
7. What methods do you use for calibration in supervised learning?
8. Can you explain the difference between Type I and Type II errors?
9. Can you explain the difference between a linked list and an array?
10. What is the convex hull?

5 knowledge-related machine learning interview questions and sample answers

Working in any industry requires specific knowledge and machine learning is no different. We’ve selected five questions from the list above and provided sample answers to help you understand your candidates’ experience in machine learning.

1. What is machine learning? How does it differ from data mining?

Machine learning is teaching computers to perform tasks without being explicitly programmed. The computer uses algorithms and statistical models to analyze data and identify patterns.

Data mining is finding and analyzing large batches of raw data to identify patterns and extract useful information.

Though the two may overlap in many job roles, you should look for candidates who are capable of explaining the difference to a layperson. Their response could include examples of how machine learning and data mining work to show their differences.

If a candidate can explain something like this to anyone, it shows they have a solid grasp of the key concepts of machine learning.

2. What methods do you use for calibration in supervised learning?

If you are a hiring manager who doesn’t work in this field, you may lack the technical expertise to assess your applicants’ answers – which makes preparing well for the interview stage all the more important. 

In fact, coming unprepared to interviews is among the most common interview mistakes you should avoid.  

The most important thing to know about this question is that platt calibration and isotonic regression are the two main methods for calibration in supervised learning. Though you should look for this exact answer, the candidate should be able to explain what this means in more detail.

3. Can you explain the difference between Type I and Type II errors?

In machine learning, Type I and Type II errors are two common that can occur when testing a hypothesis: 

• Type I error is a false positive error, for which the researcher concludes there is a significant difference between the groups when, in fact, there is no difference
• Type II error is a false negative error; the researcher concludes that there is no significant difference between the groups but actually there is a difference

Look for answers that include the issues with these errors and the best way to resolve them.

4. Can you explain the difference between a linked list and an array?

Arrays are a collection of elements of the same data type. They are stored in contiguous memory locations, meaning they are stored next to each other in memory.

Linked lists are a collection of nodes containing a piece of data and a pointer to the next node. Linked lists are more flexible than arrays because professionals can easily resize them and insert or delete elements without affecting the rest.

Once you know the difference between a linked list and an array, you should listen out for responses to this question that explain the difference well.

5. What is the convex hull?

The convex hull of a set of data points is the smallest shape that contains all of the points. If you’re a non-technical recruiter, you may not understand this definition, but look for answers that include examples of when the convex hull would be relevant and how it’s used.

10 technical and experience-related machine learning interview questions to ask potential candidates 

To learn about your candidates’ technical knowledge and experience, and put them to the test, ask them these 10 technical questions.

1. Can you explain the concept of batch statistical learning?
2. What is a confusion matrix?
3. Can logistic regression use more than two classes?
4. A model you created has a low bias and high variance. How would you deal with that?
5. Do you prefer model accuracy or model performance?
6. What machine learning certifications do you have?
7. Can you explain dimension reduction in machine learning?
8. What are bagging and boosting in the ensemble method?
9. What do you think are some shortcomings of a linear model?
10. Can you give me some advantages and disadvantages of using decision trees?

5 technical and experience-related machine learning interview questions and sample answers

Understanding candidates’ technical knowledge and experience is crucial in finding the right person. These sample answers to five of the questions from the previous section can help you find out more about what they know and assess their responses.

1. Can you explain dimension reduction in machine learning?

Dimension reduction (or dimensionality reduction) is a machine learning technique that reduces the number of features in a dataset.

This makes the data easier to visualize and can be an asset to some businesses looking to learn from the data set. No matter what machine learning role you are looking to fill, the candidate should have a good understanding of this and be able to use it effectively. 

Examples of how they have used dimension reduction in the past are important.

2. What are bagging and boosting in the ensemble method?

Ensemble learning combines multiple machine-learning models into just one. The goal is to increase overall performance. The term “bagging” refers to the aim of decreasing variance, while “boosting” refers to the aim of reducing bias.

Candidates should be able to explain both terms in more detail and how they would be used in a real-world scenario. The benefits of each are something to look out for in their answer.

3. What do you think are some shortcomings of a linear model?

The linear model is one of the most simple in machine learning and could be something you’re already familiar with. It’s the idea that data is linearly separable and therefore learns the weight of each feature. 

4. Can you give me some advantages and disadvantages of decision trees?

Decision trees are a machine-learning algorithm that can be used to categorize or make predictions. They work by asking a series of questions about the data before you and then using the answers to those questions to make a decision.

Using decision trees can be beneficial in almost any business, as they allow you to use data and decide based on it. However, there are several advantages and disadvantages to using them, and a candidate should be able to explain some of them. 

The answer you’re looking for here will emphasize how decision trees would benefit them in this role and benefit the business.

5. What machine learning certifications do you have?

There are many machine learning certifications out there. Though many are not technically necessary, they can add weight to the candidate’s experience and expertise.

As the person making the hiring decision, you may have a list of certifications you’re looking for, such as: 

• AWS Certified Machine Learning
• Professional Machine Learning Engineer by Google
• Microsoft Azure AI Engineer Associate
• IBM Machine Learning Professional Certificate

If the candidate has any of these, this might be a good sign. Keep in mind, however, that some of the best candidates might not have formal certifications. In this case, skills tests like our Machine Learning test can help evaluate applicants’ expertise objectively.

You should also look for reasons why candidates felt obtaining specific certifications was important and if they plan to get more in the future.

10 in-depth machine-learning interview questions about tools and techniques

Whether you’re trying to find the most knowledgeable candidate or just want to put them all to the test, here are 10 in-depth machine-learning questions to choose from.

1. Can you give examples of real-life applications of machine learning?
2. How do you deal with missing or corrupted data?
3. Is it better to have too many false negatives or false positives?
4. Which metrics measure the correlation of categorical data?
5. How do you handle high-bias errors?
6. What are entropy and epoch?
7. What is your experience working with a data visualization tool? 
8. Can you name a tool you have found useful in your previous job?
9. What is ensemble learning? Why use it?
10. What machine-learning algorithm do you use most frequently?

5 machine-learning questions and answers about tools and techniques

Sufficient knowledge of tools and techniques is crucial for machine-learning professionals. Below, you’ll find sample answers to some of the questions from the previous section to help you find out more about candidates’ experience with tools.

1. What machine-learning algorithm do you use most frequently?

There are several machine-learning algorithms, each designed to benefit the user in its own way. This question tests applicants’ machine-learning knowledge and they should be able to explain the benefits of each algorithm before telling you which one they use most frequently. 

If you have some machine-learning knowledge yourself, the algorithm they use most frequently could tell you a bit more about their experience.

2. What are entropy and epoch?

These two terms are common in machine learning, so most candidates should be able to concisely explain what they are: 

• Entropy is a measure of randomness or impurity in a dataset
• Epoch is a training algorithm’s iteration over the entire training dataset; one epoch is when the algorithm has seen all training samples once

Only some people can understand where the machine learning expert is coming from if they use complex terms of jargon. Ensuring they can speak to any team member about these topics and explain them easily is vital for ensuring they are the right person.

3. What is your experience working with a data visualization tool? 

Data visualization involves transforming data into a visual format that makes it easier to understand. Using graphs and charts helps identify trends, outliers, and patterns in data that would typically be difficult to comprehend or see.

Machine learning experts will use several data visualization tools, which can be useful to almost any business. Someone with experience working with these tools is arguably better than someone without. 

So, with this in mind, the answer you’re looking for describes their experience with these tools and how they would use them to benefit the business.

4. Can you name a tool you have found useful in your previous job?

This question can refer back to the previous question and lead to more discussion about the tools candidates have found useful in previous roles.

They can explain the benefits of a certain tool and how it helped them and the business they worked for. 

Don’t hesitate to ask for particular scenarios in their previous role where they used this tool and whether they think it would work well in this role.

5. What is ensemble learning? Why use it?

Ensemble learning is a technique where machine-learning experts train several models at once. It involves combining the predictions of multiple individual models to improve the predictive accuracy of the entire system. 

Depending on the role that you’re looking to fill, this might be relevant or not so much. However, it’s another question to help you assess candidates’ experience and expertise, so it’s worth asking.

Regarding the answer to this question, it’s more important to focus on the benefits of this technique. The candidate should be able to explain why ensemble learning is useful, i.e. that it allows professionals to make more precise predictions.

15 machine-learning interview questions about technical methods and business knowledge 

If you have some more time during the interview or want to ensure you have more information on your candidates’ business knowledge, ask them some of these questions.

1. Can you explain the bias-variance tradeoff?
2. How can you reduce dimensionality?
3. What does PCA stand for?
4. Can you explain the difference between AUC and ROC curves?
5. What is the difference between inductive and deductive machine learning?
6. How do you source your datasets to build machine learning models?
7. How do you deal with an imbalanced dataset?
8. Can you explain the default method for splitting in decision trees?
9. What does LDA stand for? 
10. Why are ensemble methods better than individual models?
11. How would your machine-learning experience help improve our marketing team’s efficiency?
12. How could you support this business?
13. Do you prefer to work in a team or alone?
14. What would your plans be for the first few months in this role?
15. Where do you see yourself in five years?

5 business-related machine-learning interview questions and answers

Use these machine-learning interview questions and sample answers with a focus on business outcomes to assess responses and decide between two similar candidates.

1. How would your machine-learning experience help improve our marketing team’s efficiency?

Many business units can support the marketing team, and a machine-learning role is no exception.

When answering this question, you should look for candidates that emphasize the importance of teams collaborating within a company and how machine learning can play a vital role in marketing decisions made in the future.

Data plays a vital part in any successful marketing campaign, as the team learns from previous projects. Machine learning can only aid in this and the right candidate should be able to tell you exactly how.

2. How could you support this business?

Considering how applicants can help your business meet targets and stay productive is important. There is no right or wrong answer here, but you should look for responses that match your company’s values.

They could talk about their skills and how their machine-learning knowledge will be invaluable to the business. Or, they could explain how they will support the business as an individual by being a good team player.

No matter their specific answer, you’re looking for passionate, enthusiastic applicants.

3. Do you prefer to work in a team or alone?

Every job role has elements of solo work, but most require to work within a team. Everyone is different and will have their preferences. However, the right candidate should be able to work independently while functioning well within a team when required. 

For example, a machine-learning engineer might spend a lot of time working on their own but need to spend time with team members when collaborating on projects. 

A candidate that puts too much emphasis on favoring one over another might be incompatible with your team dynamic or unable to be productive as an individual.

4. What are your plans for the first few months in this role?

The first few months in any role are crucial. It’s the newcomer’s chance to put their skills to the test and show their worth.

Candidates should mention ways in which they plan to support the business and how they might prioritize learning new skills to ensure they’re productive.

5. Where do you see yourself in five years?

It’s hard for anyone to know where they will be in five years, but with this question, you can learn for how long candidates could envision staying in your company.  

A good way to assess answers is to consider if applicants want to remain with your organization for a long time. Though it’s difficult to know whether they’ll actually stay, looking at their resume and checking for signs of job hopping behavior can help.

When to use machine learning interview questions in the hiring process

These questions are designed to help you understand more about the candidate in front of you and determine whether their experience, skillset, and background are suitable for the company.

When filling an open position, especially one that requires knowledge in a field like this, you need several steps to your hiring process.

Asking the right questions in an interview is essential, but getting the right people in the interview room before is just as important when hiring great candidates. 

The easiest way to achieve this is to use skill assessments to narrow down your list of applicants. Choose five skill tests from TestGorilla to make an assessment, then create a shortlist based on test results and use the machine-learning interview questions from this article to evaluate applicants during interviews.

Hire the right candidate for your machine-learning role with TestGorilla

TestGorilla can support you in narrowing down your list of potential candidates: Simply ask them to complete up to five short tests to evaluate their experience and skills.

You can test your potential interviewees on several topics, each designed to identify the best people for the job by assessing their knowledge and background. Once they have completed these tests, you simply need to look at test results to find the candidates whom you want to invite for an interview.

Why not try a free 30-minute live demo to discuss with the TestGorilla team how you can improve your hiring process? We can then support you in creating a streamlined and objective screening process through our data-driven techniques. 

Find the best potential machine-learning candidates for the position you’re looking to fill.