45 AI Interview Questions & Answers

Whether you’re just starting or have extensive experience with artificial intelligence already, we’ve listed common AI interview questions and sample answers to help you excel in your next interview.

We’ve divided each question into beginner, intermediate, and advanced categories so you can focus on questions relevant to your level of experience.

Beginner artificial intelligence interview questions

Are you applying for an entry-level role? Prepare for these beginner-level AI interview questions:

1. Explain artificial intelligence and machine learning

Sample answer: Artificial intelligence is all about creating systems that can perform tasks that normally need human intelligence, like recognizing images, making decisions, or understanding language. Essentially, it’s teaching computers to think or act in a human-like way.

Machine learning is a part of AI where we build models that can learn from data without being explicitly programmed. It’s about letting algorithms find patterns in data so they can make predictions or decisions on their own.

2. What are the three main types of machine learning?

Sample answer: The three main types of machine learning are supervised learning, where we have labeled data; unsupervised learning, where the data isn’t labeled and we look for patterns; and reinforcement learning, where the model learns by receiving rewards or penalties for actions.

3. Explain the difference between regression and classification

Sample answer: Regression is used when we want to predict a continuous value, like sales numbers, while classification is for predicting categories, like whether an email is spam or not. It’s about predicting numbers versus predicting labels.

4. What is a neural network

Sample answer: A neural network is a set of algorithms designed to recognize patterns, similar to how our brain works. It’s made up of layers of interconnected nodes that learn from data to make predictions or classifications.

5. What is gradient descent?

Sample answer: Gradient descent is an optimization technique that helps minimize the error of a model. Basically, it’s a way of iteratively adjusting the model’s parameters to find the best fit for the data, like finding the lowest point in a valley.

6. Explain the concept of a confusion matrix

Sample answer: A confusion matrix is a table that helps evaluate the performance of a classification model. It shows true positives, true negatives, false positives, and false negatives, giving you a clearer picture of where the model is making mistakes.

7. What is feature selection?

Sample answer: Feature selection is the process of choosing the most relevant features in your dataset for training. By reducing the number of features you make the model simpler, reduce overfitting, and improve its interpretability.

8. Explain the difference between bagging and boosting

Sample answer: Bagging trains multiple models in parallel and then averages their results to reduce variance. Boosting, on the other hand, trains models sequentially, with each new model focusing on the errors of the previous ones to improve accuracy.

9. What is a decision tree?

Sample answer: A decision tree is a model that splits data into branches based on feature values. It works like a flowchart, starting from the root and making decisions at each node. You follow branches until you reach a leaf node, which gives the prediction.

10. What is cross-validation

Sample answer: It’s a technique used to assess how well your model performs on unseen data. Cross-validation works by splitting the dataset into several parts, training the model on a subset, and testing it on the remaining parts. By repeating this process multiple times, it provides a more reliable estimate of how well the model will generalize to new data.

11. Explain bias in machine learning

Sample answer: Bias is when a model makes simplifying assumptions about a complex problem, which can lead to errors. High bias can make the model underfit, meaning it doesn’t capture the underlying trends in the data well enough.

12. What is variance in machine learning?

Sample answer: Variance refers to how much the model’s predictions change when trained on different datasets. High variance means the model is too sensitive to the training data which leads to overfitting, where it performs well on training data but poorly on new data.

Low variance means that the model’s predictions are consistent across different training datasets. However, models with very low variance might also have high bias which can lead to underfitting.

13. What is a dataset?

Sample answer: A dataset is a collection of data used to train and evaluate machine learning models. It contains features and, in the case of supervised learning, labels. Good datasets are essential for creating effective models that can generalize well to new data.

14. Explain the purpose of a validation set

Sample answer: A validation set is used to fine-tune a machine learning model. It helps in evaluating the model’s performance during training, adjusting hyperparameters, and preventing overfitting before testing the model on unseen data.

15. What is feature extraction in machine learning?

Sample answer: It’s the process of transforming raw data into a set of meaningful features that can be used by a model. It helps in simplifying the data while retaining important information, which makes model training faster and more effective.

Intermediate artificial intelligence interview questions

If you’re applying for a mid-level position, these are some questions you might get asked during an interview:

1. How does a random forest work?

Sample answer: A Random Forest is an ensemble method that creates multiple decision trees using different subsets of the data. Each tree makes a prediction, and the final prediction is either the average (for regression) or the majority vote (for classification). This approach helps improve accuracy and reduce overfitting.

2. Explain the concept of feature engineering

Sample answer: It’s about creating new features or transforming existing ones to improve model performance. It can involve normalizing data, one-hot encoding categorical variables, or combining features to make them more informative. Good feature engineering can make a huge difference in model accuracy.

3. What is the purpose of regularization?

Sample answer: Regularization helps prevent overfitting by adding a penalty term to the model’s loss function. This penalty discourages the model from learning overly complex patterns.

4. Explain the concept of principal component analysis

Sample answer: PCA is a technique used to reduce the dimensionality of your data while retaining as much information as possible. It transforms the data into a new set of uncorrelated variables called principal components, which makes it easier to visualize and speeds up model training.

5. What are activation functions in neural networks?

Sample answer: Activation functions introduce non-linearity into neural networks, which helps them learn complex relationships. Without them, the network would just be a linear model.

6. What is a convolutional neural network

Sample answer: A CNN is a type of deep learning model that’s especially good at processing images. It uses convolutional layers to extract features like edges and textures from images, allowing it to recognize more complex patterns at later layers.

7. Explain K-means clustering

Sample answer: K-means clustering is an unsupervised learning algorithm that groups data into clusters. It assigns each point to the nearest cluster centroid and then updates the centroids iteratively until the clusters are stable. It’s commonly used for tasks like customer segmentation.

8. What is the role of hyperparameter tuning in machine learning?

Sample answer: Proper tuning can significantly improve the accuracy and efficiency of a model. It involves adjusting the settings that govern the training process of a model, such as the learning rate, number of layers, or regularization strength, for example.

9. What are word embeddings?

Sample answer: Word embeddings are vector representations of words that capture their meanings and relationships with other words. These embeddings are useful in NLP tasks because they help models understand the context and semantic similarity between words.

10. What is the vanishing gradient problem?

Sample answer: It happens when gradients become very small during backpropagation in deep networks, making it difficult for the model to learn. It’s common with activation functions like sigmoid. There are a few ways to address this, for example using ReLU or techniques like batch normalization to stabilize training.

11. How does LSTM solve the problem of long-term dependencies?

Sample answer: They do it by using special gates to control the flow of information. These gates decide what to remember and what to forget, allowing Long Short-Term Memory networks to retain information over longer sequences, which is crucial for tasks like time-series prediction.

12. What is ROC curve and AUC?

Sample answer: A Receiver Operating Characteristic curve is a plot that shows the performance of a classification model at all thresholds, comparing true positive rates to false positive rates.

Area Under the Curve quantifies the overall performance of the model. A higher AUC means better discrimination between positive and negative classes.

13. What is dropout in deep learning?

Sample answer: It’s a regularization method where random neurons are dropped during training. This prevents them from becoming overly reliant on each other, encouraging the network to learn more robust features.

14. What is the purpose of using different types of cross-validation?

Sample answer: Different types of cross-validation help ensure that your model generalizes well to new data. For example, K-fold splits data into k parts, testing each fold once. Leave-one-out uses each instance for testing, ideal for small datasets. Stratified, on the other hand, ensures each fold has a balanced distribution of target classes, improving evaluation for imbalanced data.

15. What is the role of data normalization in machine learning?

Sample answer: Data normalization is a preprocessing step used to rescale features to a common range, typically between 0 and 1. This ensures that no single feature dominates the learning process due to its scale, which leads to faster convergence and improved model performance.

It’s especially important for algorithms like gradient descent and k-nearest neighbors.

Advanced artificial intelligence interview questions

Below are 15 interview questions you might get asked if you have several years of experience working with AI:

1. How does batch normalization work?

Sample answer: It normalizes the inputs of each layer so that they have a mean of zero and a variance of one, helping stabilize training and allowing for higher learning rates. By reducing internal covariate shift, it speeds up convergence and acts as a regularizer, reducing the need for other forms of regularization.

2. Explain transfer learning and how it can be used

Sample answer: Transfer learning is when you use a pre-trained model on a similar problem instead of starting from scratch. For example, you can take a model trained on millions of images and fine-tune it for your specific image classification task. It’s especially useful when you have limited data because it saves time and often improves accuracy.

3. How does backpropagation work?

Sample answer: Backpropagation is the process of calculating gradients for each weight in a neural network to minimize the loss function. It involves propagating the error backward from the output layer to the input layer, updating the weights along the way using the chain rule.

4. What is the purpose of reinforcement learning?

Sample answer: Reinforcement learning refers to training an agent to take actions in an environment to maximize cumulative rewards. Unlike supervised learning, where you have labeled data, RL learns by trial and error, receiving feedback from the environment. It’s used in fields like robotics, gaming, and decision-making systems where actions affect future states.

5. What is the difference between L1 and L2 regularization?

Sample answer: L1 regularization adds the absolute values of the weights as a penalty, encouraging sparsity by forcing some weights to zero, which is useful for feature selection. This helps simplify the model and can make it more interpretable by effectively reducing the number of features.

Meanwhile, L2 regularization adds the square of the weights, which discourages large weight values, encouraging smaller weights overall and making the model more stable and less sensitive to small fluctuations in the training data. L2 regularization helps to reduce overfitting by ensuring that the model does not become too complex and over-adapt to the training data, promoting better generalization to unseen data.

6. How does BERT work for NLP tasks?

Sample answer: Bidirectional Encoder Representations from Transformers is a transformer-based model that reads text both left-to-right and right-to-left to understand context. It’s pre-trained on large amounts of data and can then be fine-tuned for specific NLP tasks, like sentiment analysis or question answering.

Its bidirectional nature helps it capture more nuanced meanings compared to models that only consider one direction, such as left-to-right or right-to-left context.

7. For self-attention, is it possible to switch the Q and K?

Sample answer: While it’s technically possible to switch the two, the roles of the query (Q), key (K), and value (V) vectors are distinct and crucial to the operation of the mechanism. Switching the query and key vectors would fundamentally alter the behavior of the attention mechanism and lead to incorrect or unintended results.

8. Explain the attention mechanism in neural networks

Sample answer: The attention mechanism allows a model to focus on the most relevant parts of an input sequence, assigning different weights to different inputs. It’s particularly useful in NLP tasks like translation, where understanding the context of each word in a sentence is key.

9. What is a Generative Adversarial Network?

Sample answer: A GAN consists of two networks, a generator and a discriminator, that compete against each other. The generator creates fake data, while the discriminator tries to distinguish between real and fake data. This adversarial process helps the generator improve, leading to the creation of highly realistic data.

10. What are autoencoders and how are they used?

Sample answer: Autoencoders are neural networks that learn to encode data into a lower-dimensional form and then reconstruct it. They’re useful for dimensionality reduction, denoising, and learning latent representations. The encoder compresses the input, and the decoder tries to recreate it, helping the model understand important features of the data.

11. What is the Transformer architecture and why is it important in NLP?

Sample answer: The Transformer architecture is a type of neural network model introduced to handle sequence data, particularly for NLP tasks.

It uses self-attention mechanisms to capture relationships between all tokens in a sequence simultaneously, making it highly effective for tasks like translation and text generation. Its parallel processing capability also allows for faster training compared to recurrent architectures.

12. Explain the difference between Huber loss and MSE

Sample answer: Huber loss is a combination of mean squared error and mean absolute error. It behaves like MSE when the error is small, providing a smooth gradient for optimization, and switches to MAE for larger errors, making it less sensitive to outliers. This dual behavior makes Huber loss particularly useful when you need a balance between robustness to outliers and maintaining a stable gradient, which helps in achieving smooth and efficient optimization.

It’s often used in regression tasks where outliers might otherwise skew the results, but a completely insensitive approach like MAE would hinder convergence.

13. What is the purpose of using the ReLU activation function?

Sample answer: ReLU introduces non-linearity by outputting zero for negative values and the input itself for positive values. It’s computationally efficient and helps mitigate the vanishing gradient problem, making it a popular choice in deep learning, especially for convolutional layers.

14. What are the limitations of deep learning models?

Sample answer: Deep learning models need a lot of data and computational resources to train effectively. They’re often seen as black boxes, making their decision-making hard to interpret.

Additionally, hyperparameter tuning can be challenging, and they’re prone to overfitting without enough data or proper regularization. They also struggle in low-data environments and require high-quality data to perform well.

15. What is the role of positional encoding in Transformer models?

Sample answer: It’s used to provide information about the relative position of tokens in a sequence. Since Transformers do not inherently understand the order of tokens, positional encodings are added to input embeddings to help the model capture the sequential relationships between tokens, which is necessary for tasks like language translation and text generation.

Frequently asked questions about AI interviews

Here are answers to three of the most commonly asked questions we receive about AI interviews:

How do I prepare for an AI interview?

To prepare for an AI interview, review key concepts like supervised and unsupervised learning, algorithms such as decision trees and neural networks, and essential math skills like linear algebra, calculus, and statistics.

You can gain hands-on experience by working on personal or open-source projects. Additionally, practice solving coding problems to ensure you’re ready for your interview.

What questions should I ask in an AI interview?

Here are some questions you should ask in an AI interview:

1. How do you measure project success?
2. What tools and technologies does the team use?
3. Can you describe the projects currently being worked on?
4. What does the team structure look like?
5. How does the team ensure model interpretability and ethics in AI applications?

These questions will give you a better understanding of the work you’ll be doing at the company and what hard and soft skills you’ll need.

How do you nail an AI interview?

You nail an AI interview by preparing thoroughly for both the technical and non-technical part of the interview. While your technical skills are usually most important to employers, other factors like cultural fit, passion for the job, and soft skills are also taken into consideration.

So prepare to answer common interview questions about work style, what you know about the company, and how the role you’re applying for aligns with your long-term goals.