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Technical and programming interview questions are common for machine learning engineer roles. Hiring managers use interviews to assess a qualified candidate's knowledge of fundamental machine learning methods and concepts. Experience and certifications in machine learning (ML) can open doors to many jobs, including machine learning engineer, data scientist, cyber security analyst, cloud architect, and more. If you're preparing for an interview relating to machine learning, there are some common questions that you may encounter. Depending on the role, the questions you're asked may vary.
Explain the difference between deep learning, artificial intelligence (AI), and machine learning.
This question tests the candidates' knowledge in the field. The interviewer may want to know whether the candidate can explain the subtle differences between each concept to ensure that they do have a strong grasp of foundational machine learning knowledge. A great answer to this question would make clear that machine learning is a subset of AI and that deep learning is a subset of machine learning by describing each. Rather than just stating the obvious, the candidate should use examples in their response to show that they have total mastery of these important concepts.
How do you choose the algorithm to use for a dataset?
With this question, the interviewer wants to understand the candidates' knowledge of some basic functions of ML. Here the candidate should give an example of how they would make a choice. Typical answer for this question would be like "The type of algorithm depends on the type of data I receive. For example, if the data is organised linearly, I would use linear regression. But if the data comprises non-linear interactions, boosting or bagging regression would be ideal. I would choose a neural network when working with images."
How do you handle missing or corrupted data in a data set?
This question helps demonstrate the candidates' problem-solving skills and experience in dealing with corrupted data. At the most basic level, this question is asked to understand the process of how the candidate sees a problem. A great way to answer this question is to suggest methods that may solve the problem. It’s a good idea to include examples and more than one solution to help show your understanding of datasets. At the same time, make sure that you emphasize the concrete steps you would take to solve these kinds of problems, so that the interviewer can get a clearer picture of what you look like when you're in your element.
Describe your favorite machine learning algorithm.
This question is an opportunity for the candidate to show their preferences and individual skills while also showing that they have a deep understanding of various ML algorithms. Some common machine learning algorithms you might consider include Linear regression, Logistic regression, Naive bayes, Decision trees, Random forest algorithm, K-nearest neighbor (KNN), and K means.
Here the exact algorithm mentioned isn't as important as the reasons given for selecting it. The candidates should use this question as an opportunity to showcase their knowledge of the field by drawing direct comparisons to other algorithms, so it’s clear their expertise extends further than the ML algorithm they are highlighting. Also while answering this question, the candidate should use examples from their career and studies to support their answer. Focusing on concrete examples will also allow the candidate to highlight the work they've already done that can prepare them for the job.
What's the difference between unsupervised learning and supervised learning?
This is another common question aimed at assessing the candidates' understanding of foundational machine learning techniques, which will likely undergird much of their future work. A great way to answer this question is by making a clear distinction between labeled and unlabeled training data sets, and how they're used to create different machine learning models. The candidate might also consider highlighting any machine learning projects they have undertaken and explaining how they used either supervised or unsupervised learning to accomplish them.
Which machine learning algorithm do driverless cars use?
The interviewer may ask such questions to gauge the candidates' ability to understand and grasp machine learning in real-world applications. Such questions also test the candidates' ability to remain updated on the latest trend in the industry. Answer for this question would be like "Machine learning algorithm used in driverless cars is object tracking. The algorithm helps improve the accuracy of profiling and helps distinguish between different objects, whether it is a human, another vehicle or an animal. Driverless cars also use pattern recognition algorithms in which you feed many datasets containing objects to train the algorithm."
Under what circumstances would you prefer a Ridge regression over a Lasso regression?
Such questions help an interviewer understand the candidates' ability to use algorithms and formulas in different situations. The candidates' answer requires explaining when they would use each of these methods. Answer for this question would be like "The type of regression I select depends upon the parameters, coefficients and variables of the model. Usually, I use the Lasso regression when the model has fewer variables with large or medium-sized effects. But when the model has many variables with small or medium-sized effect, I use the Ridge regression."
How do you identify outliers in a dataset?
The interviewer wants to understand the candidates' ability to apply their theoretical knowledge to produce practical and real-world conclusions. For a compelling answer, the candidate should use a professional experience from your previous or current job. Typical answer for this question would be like "Usually, I analyse the raw data trend to understand the general trends. Based on my analysis, I decide on the method for identifying outliers. In my previous job, I compiled data of females using the train services based on the time spent by them on every trip. I found the outliers and used a statistical technique like quartile to check the accuracy of my findings."
Would you prefer many small decision trees or a single large decision tree for building an ML model?
Decision trees helps to evaluate different options to structure and build the ML model. With this question, the interviewer wants to understand the candidates' level of expertise with this topic. Answer for this question would be like "I would prefer many small decision trees because it is the same as using a random forest model. This model is more precise, has a low bias and is less prone to the common problem of overfitting when compared to a single large decision tree."
Are recall and true positive rate related?
Through this question, the interviewer wants to test the candidates' knowledge in two different aspects of ML. The answer the candidate gives requires explaining how these two relate to each other. Answer for this question would be like "True positive rate is equal to recall. These two terminologies are related because they are the same and have the same formula. Both are used for measuring the percentage of actual positives that are correctly identified."
The best way to ace a machine learning interview (or any interview) is to prepare for it in advance. In the interview, the candidate should make sure to connect their answers with real-life examples, especially ones that reference their previous or current work. Recruiters usually look for experience as well as knowledge, and the more experience the candidate can demonstrate, the more they'll be able to highlight their preparedness for the job.
It’s also beneficial to show that you’re always learning and developing your skills. The candidate should show how driven they are to improve themselves and their expertise during the interview process.
Also make sure to properly research the company before you go to the interview. This will allow you to tailor your responses and examples to what the company does.
