50 interview questions to evaluate AI proficiency

What does AI proficiency involve?

A truly proficient AI professional doesn't just "vibe code" to create an application using an LLM; they understand the entire machine learning lifecycle. This starts with sourcing and cleaning data, moves through designing and training models, and ends with deploying, monitoring, and ensuring ethical oversight of the system in production.

There's a big difference between knowing about AI and understanding how to apply it to create business value. A candidate needs both deep theoretical knowledge and practical skills to build robust, scalable, and responsible AI solutions. It also includes the increasingly important skill of collaborating with AI tools to deliver results faster.

Why assessing AI proficiency is essential

The AI talent market has polarized. There’s a surplus of candidates with foundational knowledge but a severe shortage of experts who can deliver production-ready systems. Here’s why a rigorous assessment matters so much:

• The field evolves at breakneck speed. New frameworks, methods, and ethical concerns appear constantly. A resume from last year is probably already out of date.

• AI resumes often overstate real experience. Many candidates list tools like TensorFlow or PyTorch but lack deep, hands-on experience. Therefore, interviews must probe for applied knowledge.

• AI roles demand both theory and practice. A candidate must understand the math behind the models and the engineering challenges of deployment, scalability, and bias mitigation. This means interview strategies are just one part of hiring for AI proficiency.

50 interview questions to evaluate AI proficiency

These questions are designed to test a candidate’s practical knowledge across 10 critical areas of AI. Focus on the categories important to your business to shortlist candidates most relevant to your open role.

Questions about foundational AI and ML concepts

This set of questions tests a candidate's core knowledge. They ensure your candidate has the academic grounding needed to build reliable machine learning models. A candidate who understands these concepts can make better decisions when faced with real-world trade-offs.

1. Explain the bias-variance tradeoff. Can you describe a scenario where you would intentionally choose a model with higher bias?

2. You are building a model to predict a rare disease where only 1% of the patients in the dataset have it. Why is accuracy a poor evaluation metric for these kinds of imbalanced datasets? What metrics would you use instead?

3. What is data leakage? Describe a common way it occurs during preprocessing and how you would prevent it using a scikit-learn Pipeline.

4. Explain the difference between supervised, unsupervised, and reinforcement learning. Give a business example for each.

5. What is the purpose of a validation set, and how does it differ from a test set?

Questions about machine learning algorithms and techniques

A strong candidate needs to be fluent in a range of machine learning models. These questions assess their ability to select, implement, and optimize the right algorithm for a given problem.

1. Explain how a Random Forest model works. Why is it generally more robust than a single decision tree?

2. Compare and contrast gradient boosting machines (like XGBoost) and random forests. When would you choose one over the other?

3. What is the "kernel trick" in Support Vector Machines (SVMs) and why is it useful?

4. You've used K-Means clustering, a popular unsupervised learning algorithm, but the results are poor. What are some likely reasons K-Means failed, based on its underlying assumptions? What algorithm might you try next?

5. What are the key differences between L1 and L2 regularization? How do they affect the model's weights?

Questions about deep learning and neural networks

Deep learning powers the most advanced AI today. These questions validate a candidate's experience with frameworks like PyTorch or TensorFlow and their theoretical understanding of neural networks.

1. What is the role of a non-linear activation function in a neural network? What would happen if you removed all of them?

2. Explain the self-attention mechanism in a Transformer model. Why was it such a breakthrough compared to RNNs for processing sequential data?

3. You're training a deep neural network, but the training loss is decreasing very slowly. What are three potential reasons, and how would you address them?

4. Your RNN model performs well on short text sequences but fails on longer ones. What is this problem called, and what architecture (like LSTM or GRU) would you use to fix it?

5. How would you design a neural network to classify video clips? Explain how you’d handle both the spatial and temporal aspects of the data.

Natural language processing (NLP) questions

NLP has been transformed by large language models (LLMs). These questions identify candidates who can work with modern NLP tools on tasks like building chatbots or analyzing human language for sentiment analysis. A great way to pre-screen these skills is with an NLP test.

1. Design a customer service chatbot that must answer questions using the company's internal documents. Why would you choose a Retrieval-Augmented Generation (RAG) approach over just fine-tuning an LLM?

2. Explain the difference between a BERT-based model and a GPT-based model. For a sentiment analysis task, which would you choose and why?

3. What is "hallucination" in the context of LLMs? Describe two different technical strategies you could use to reduce it.

4. What are tokenization and embeddings? Why are subword tokenizers like BPE common in modern LLMs?

5. Explain transfer learning and its importance in modern NLP.

Computer vision questions

These questions assess the skills required to work with image and video data. They focus on core tasks, such as image classification and object detection, and key tools like OpenCV. To dig deeper, consider adding a Computer Vision test to your hiring process.

1. Compare a single-stage object detector like YOLO with a two-stage detector like Faster R-CNN. What are the primary tradeoffs?

2. You need to build a system that automatically detects when store shelves are empty. Describe the computer vision pipeline you would create.

3. Explain the difference between image classification, object detection, and image segmentation.

4. What is data augmentation in the context of computer vision? Why is it a fundamental step when training vision models?

5. How have diffusion models improved upon Generative Adversarial Networks (GANs) for high-fidelity image generation?

Reinforcement learning (RL) questions

RL is used to train agents for tasks in control systems, robotics, and simulations. These questions probe a candidate's knowledge of the unique RL framework, from Q-learning to the policy gradient methods that train today’s most advanced models.

1. Frame the problem of teaching an AI to play chess as a reinforcement learning problem. Define the agent, environment, state, action, and a possible reward function.

2. Compare and contrast Q-learning (value-based) and Policy Gradient (policy-based) methods. When is one preferred over the other?

3. What is the exploration-exploitation dilemma in reinforcement learning?

4. Explain the role of Reinforcement Learning from Human Feedback (RLHF) in training modern LLMs like ChatGPT.

5. Why are algorithms like PPO often preferred over vanilla policy gradient methods for training RL agents?

AI ethics, fairness, and bias questions

These questions explore a candidate's awareness of model explainability, data bias, and responsible AI practices to ensure they build technology that is fair and safe.

1. A colleague suggests making the hiring model fair by removing a column from the data. Why is this "fairness through unawareness" approach flawed?

2. Explain the difference between two fairness metrics, such as Demographic Parity and Equalized Odds. Why is it often impossible to satisfy both simultaneously?

3. You need to explain a loan denial prediction to a non-technical bank manager. Would you use LIME or SHAP? Justify your choice.

4. What are the ethical considerations you'd weigh when designing a content moderation system for a social media platform?

5. What is the purpose of a "model card" or "datasheet for datasets"?

Questions about AI programming, tools, and frameworks

Proficiency requires fluency with the tools of the trade, including programming languages like Python, cloud platforms like AWS or GCP, and MLOps tools. These questions ensure a candidate can be productive in a real-world development workflow.

1. Python is the dominant language in AI. What makes it so well-suited for machine learning development?

2. Compare PyTorch and TensorFlow. What are the strengths of each, particularly regarding research flexibility versus production deployment?

3. Describe how you would set up a CI/CD pipeline for a machine learning model. What tools would you use for experiment tracking and model versioning?

4. You need to train a large model on a 100GB dataset. Your local machine runs out of memory. What frameworks or platforms would you use to handle this scale?

5. What is the role of containerization with Docker in MLOps?

Data handling and feature engineering questions

"Garbage in, garbage out" is the rule in machine learning. A candidate’s ability to clean messy data and create robust features is one of the most critical AI skills. These questions test their hands-on experience with pre-model data pipelines.

1. You are given a raw dataset with missing values, outliers, and inconsistent categorical labels. Describe your step-by-step cleaning process.

2. What is feature engineering? Given a dataset for predicting house prices, what are two new features you could create to improve model performance?

3. Why is it a mistake to perform data preprocessing (like scaling or imputation) before splitting your data into training and testing sets?

4. When would you use one-hot encoding versus label encoding for a categorical feature? What are the risks of using one-hot encoding on a high-cardinality feature?

5. What are feature stores, and what problem do they solve in a large-scale ML environment?

AI project design and system thinking questions

These questions identify candidates who can act as architects, not just builders. They test for the ability to design scalable, maintainable, and impactful end-to-end AI systems, a key skill when you hire a machine learning engineer.

1. Design an end-to-end system for a real-time ad-click prediction service. Walk me through the full pipeline, from data ingestion to model monitoring.

2. The recommendation engine you built works for 1,000 users but needs to serve 10 million. What parts of the system are likely bottlenecks, and how would you re-architect it for scale?

3. You've deployed a fraud detection model. What is your monitoring plan for the next six months? What is model drift, and how would you detect it?

4. You can deploy a simple, fast, and interpretable statistical model, such as logistic regression, or a "black box" deep learning model that is 5% more accurate. Describe a business scenario where you would choose the simpler model.

5. What is MLOps, and why is it essential for building reliable AI products at scale?

Go further with skills-based testing

Great interview questions are a start. But interviews are subjective and can be prone to bias – in fact, 42% of job seekers report experiencing bias in the hiring process. To get a complete picture of a candidate's abilities, you need objective evidence of their skills.

By combining structured interviews with talent assessments, you can validate a candidate's true proficiency. Employers who use skills-based hiring are nearly twice as likely to be “very satisfied” with their hires compared to those who do not.

TestGorilla offers a wealth of tests perfect for AI positions, including:

• Artificial Intelligence test

• Machine Learning test

• Data Science test

• Deep Learning test

• Neural Networks test

• Natural Language Processing (NLP) test

• Computer Vision test

• Reinforcement Learning (RL) test

Our library of more than "350+" scientifically validated tests helps you build a complete assessment. 

TestGorilla helps you find applicants with real AI knowledge

Finding top AI talent requires moving beyond traditional resume-based approaches and unstructured interviews. The right set of interview questions can help you separate candidates who know the buzzwords from those who have the practical problem-solving skills to make an impact. 

For the most complete and objective evaluation, combine your expert-led interviews with skills assessments. See our guide on in-demand AI skills to assess effectively, and use an Artificial Intelligence test to pinpoint top candidates.

If you're ready to get started, try TestGorilla for free today.