Essential AI interview questions and how to answer them
What common AI interview questions do employers ask potential candidates? Learn what questions to expect so you can prepare to ace your job interview.
By: Amy Boyington, Edited by: Marie Custodio Collazo
Published: September 17, 2025
As artificial intelligence becomes more intertwined with everyday tasks, including writing emails and organizing schedules, AI professionals are more in demand than ever. With diverse roles to fill across data science, AI research, and machine learning engineering, companies are looking for stand-out candidates who demonstrate technical expertise and the ability to solve real-world problems.
Explore this guide to learn about commonly asked AI interview questions and how to approach them effectively to showcase your skills with confidence.
General AI interview questions
Employers may start by asking a few general AI interview questions to learn about your experience with AI and where your strengths lie. These general AI questions can help hiring managers better understand your foundational skills and how you apply them to different scenarios.
What is the difference between AI, machine learning, and deep learning?
All AI professionals should understand the nuances of AI, machine learning, and deep learning, and how they work together. Give short but clear definitions of each and explain how the industry uses each. Consider providing a strong example of when you might rely on one over the other two.
Can you walk me through a recent AI project you've worked on?
The STAR method — situation, task, action, result — can help you structure your answer to this question so an interviewer can gauge your real-world AI experience. First, choose an AI project that emphasizes measurable results. Then, walk the interviewer through what you did and how you handled it, followed by the clear outcome.
How do you evaluate the performance of an AI model?
Prove your understanding that AI model performance metrics vary by task and goals. Note specific metrics you feel most confident discussing, like accuracy and false positive rate. Explain when you might rely on one over the other, such as diagnostic tools, which can lead to incorrect diagnoses if inaccurate.
Machine learning and modeling questions
These AI interview questions dig deeper into your technical knowledge to determine how well you understand machine learning capabilities and can apply theory to real scenarios. Your answers should offer clarity and confidence to prove your technical expertise and ability to communicate complex ideas.
What are the pros and cons of decision trees versus neural networks?
Generally, decision trees classify data and map potential outcomes, while neural networks help identify patterns within datasets by continuously learning. Give examples of use cases for each, such as a call center using a neural network to route calls automatically.
Explain the bias-variance tradeoff.
Briefly define bias-variance before applying the concept in a solutions-oriented way with an example, sketch, or chart. Also, demonstrate underfitting and overfitting using a simple visual or analogy. For example, a highly fitted t-shirt can be restrictive, while a very loose t-shirt allows for too much flexibility or variance.
How do you prevent overfitting in machine learning models?
Discuss a few reasons why overfitting occurs, like training on noisy data or limited data sets. Then, offer realistic prevention solutions, such as timing the training phase to stop model training before it picks up noisy data or augmenting training data through small, varied changes before processing.
Data handling and feature engineering
Employers will use questions like those below to determine how well you can prepare and refine data for modeling. Your answers should highlight your understanding of practical techniques and your ability to justify your choices, so consider weaving real-world examples into your responses.
How do you handle missing or imbalanced data?
Explain how messy data can result in modeling biases. Then, mention specific ways to handle imbalanced data at the data level and algorithm level, such as oversampling and anomaly detection, as well as missing data, like k-nearest neighbors (KNN) imputation. Wrap up your answer by describing a time you've used one of these methods and how it helped.
What is feature selection, and why is it important?
Briefly define feature selection and how it reduces irrelevant or redundant inputs to improve model accuracy. Then, focus most of your response on feature selection methods, such as Fisher's score, backward selection, and principal component analysis, while explaining the difference between supervised and unsupervised feature selection. Make it clear when you might choose one technique over another.
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