100 AI interview questions and answers for 2025

Activation functions play a vital role in neural networks, serving as a non-linear transformation applied to the output of a neuron or node. They determine the output of a neuron based on the weighted sum of its inputs, introducing non-linearity into the network. The inclusion of activation functions allows neural networks to model complex, non-linear relationships in the data.

Gradient descent is a popular optimization algorithm that is used to find the minimum of a function iteratively. It's widely used in machine learning and deep learning for training models by minimizing the error or loss function, which measures the difference between the predicted and actual values.

Data normalization is a pre-processing technique used in machine learning and statistics to standardize and scale the features or variables in a dataset. The purpose of data normalization is to bring different features or variables to a common scale, which allows for more accurate comparisons and better performance of learning algorithms.

The main purposes of data normalization are:

Improving model performance: Some machine learning algorithms, like gradient-based optimization methods or distance-based classifiers, are sensitive to the feature scale.

Ensuring fair comparison: Normalization brings all features to a comparable range, mitigating the effect of different magnitudes or units of measurement, and ensuring that each feature contributes equally to the model's predictions.

Faster convergence: Gradient-based optimization algorithms can converge faster when data are normalized, as the search space becomes more uniformly scaled and the gradients have a more consistent magnitude.

Reducing numerical issues: Normalizing data can help prevent numerical issues like over- or underflow that may arise when dealing with very large or very small numbers during calculations.

Some common activation functions include sigmoid, tanh, and ReLU.

Sigmoid: Maps the input to a value between 0 and 1, allowing for smooth gradient updates. However, it suffers from the vanishing gradient problem and is not zero-centered.

Tanh: Maps the input to a value between -1 and 1, providing a zero-centered output. Like the sigmoid function, it can also suffer from the vanishing gradient problem.

ReLU (Rectified Linear Unit): Outputs 0 for negative input values and retains the input for positive values. It helps alleviate the vanishing gradient problem and has faster computation time, but the output is not zero-centered and can suffer from the dying ReLU issue.

Data augmentation is a technique used to increase the amount of data available for training a machine learning model. This is especially important for deep learning models, which require large amounts of data to train.

The Swish function is an activation function. It is a smooth, non-linear, and differentiable function that has been shown to outperform some of the traditional activation functions, like ReLU, in certain deep learning tasks.

Forward propagation is the process of computing the output of a neural network given an input. Forward propagation involves passing an input through the network, layer by layer, until the output is produced. Each layer applies a transformation to the output of the previous layer using a set of weights and biases. The activation function is applied to the transformed output, producing the final output of the layer.

On the other hand, backpropagation is the process of computing the gradient of the loss function with respect to the weights of the network. It is used to update the weights and biases of the network during the training process. It involves calculating the gradient of the loss function with respect to each weight and bias in the network. The gradient is then used to update the weights and biases using an optimization algorithm such as gradient descent.

Classification is a type of supervised learning task in machine learning and statistics, where the objective is to assign input data points to one of several predefined categories or labels. In a classification problem, the model is trained on a dataset with known labels and learns to predict the category to which a new, unseen data point belongs. Examples of classification tasks include spam email detection, image recognition, and medical diagnosis.

Some benefits of classification include:

Decision-making: Classification models can help organizations make informed decisions based on patterns and relationships found in the data.

Pattern recognition: Classification algorithms are capable of identifying and learning complex patterns in data, enabling them to predict the category of new inputs accurately.

Anomaly detection: Classification models can be used to detect unusual or anomalous data points that don't fit the learned patterns.

Personalization and recommendation: Classification models can be used to tailor content and recommendations to individual users, enhancing user experiences and increasing engagement.

Convolutional neural networks are a type of neural network that is well-suited for image classification tasks. In classification, the model learns to classify input data into one or more predefined classes or categories based on the features of the data. There are various benefits of classification, and it has numerous practical applications in different fields, such as:

Object Recognition: It is used in image and speech recognition to identify objects, faces, or voices.

Sentiment Analysis: It helps understand the polarity of textual data, which can be used to gauge customer feedback, opinions, and emotions.

Email Spam Filtering: It can be used to classify emails into a spam or non-spam categories to improve email communication.

Autoencoders are a type of neural network that is used for dimensionality reduction. The different types of autoencoders include Denoising, Sparse, Undercomplete, etc.

Denoising Autoencoder: It is used to achieve good representation, meaning it can be obtained robustly from a corrupted input, which will be useful for recovering the corresponding clean input.

Sparse Autoencoder: This has a sparsity penalty, a value close to zero but not exactly zero. It is applied on the hidden layer in addition to the reconstruction error, which prevents overfitting.

Undercomplete Autoencoder: This does not need any regularization because they maximize the probability of data rather than copying the input to the output.

Fuzzy approximation theorem states that a function can be approximated as closely as desired using a combination of fuzzy sets. The theorem states that any continuous function can be represented as a weighted sum of linear functions, where the weights are fuzzy sets that capture the input variables' uncertainty.

LSTM stands for Long Short-Term Memory. It is a neural network architecture that is used for modeling time series data. LSTM has three main components:

The forget gate: This gate decides how much information from the previous state is to be retained in the current state.

The input gate: This gate decides how much new information from the current input is to be added to the current state.

The output gate: This gate decides what information from the current state is to be output.

Transfer learning is a machine learning technique where you use knowledge from one domain and apply it to another domain. This is usually done to accelerate the learning process or to improve performance.

There are several benefits of transfer learning:

Learn from smaller datasets: If you have a small dataset, you can use transfer learning to learn from a larger dataset in the same domain. This will help you to build better models.

Learn from different domains: You can use transfer learning to learn from different domains. For example, if you want to build a computer vision model, you can use knowledge from the medical domain.

Better performance: Transfer learning can help you to improve the performance of your models and apply it on other domains to build better models.

Pre-trained models: If you use a pre-trained model, you can save time and resources. This is because you don’t have to train the model from scratch.

Use of fine-tune models: You can fine-tune models using transfer learning. Also, you can adapt the model to your specific needs.

The cost/loss function is an important part of machine learning that maps a set of input parameters to a real number that represents the cost or loss. The cost/loss function is used for optimization problems. The goal of optimization is to find the set of input parameters that minimize the cost/loss function.

Epoch, batch, and iteration are all important terms in machine learning. Epoch refers to the number of times the training dataset is used to train the model; Batch refers to the number of training samples used in one iteration; Iteration is the number of times the training algorithm is run on the training dataset.

Dropout is a method used to prevent the overfitting of a neural network. It refers to dropping out some neural network units. The process is similar to that of natural reproduction, where distinct genes combine to produce offspring while the other genes are dropped out instead of strengthening their co-adaptation.

As more layers are added and the distance from the final layer increases, backpropagation is not as helpful in sending information to the lower layers. As a result, the information is sent back, and the gradients start disappearing and becoming small in relation to network weights. These disappearing gradients are known as vanishing gradients.

Batch gradient descent is an optimization algorithm that calculates the gradient of the cost function with respect to the weights of the model for each training batch. The weights are updated in the direction that decreases the cost function.

Ensemble learning is a method of combining multiple models to improve predictive accuracy. These methods usually cost more to train but can provide better accuracy than a single model.

One of the biggest drawbacks of Machine learning is that it can be biased if the data used to train the algorithm is not representative of the real world. For example, if an algorithm is trained using data that is mostly from one gender or one race, it may be biased against other genders or races.

Here are some other disadvantages of Machine Learning:

• Possibility of high Error
• Algorithm selection
• Data acquisition
• Time and space
• High production costs
• Lacking the skills to innovate

Sentiment analysis is the process of analyzing text to determine the emotional tone of the text in NLP. This can be helpful in customer service to understand how customers are feeling, or in social media to understand the general public sentiment about a topic.

Breadth-First Search (BFS) and Depth-First Search (DFS) are two algorithms used for graph traversal.
BFS algorithm starts from the root node (or any other selected node) and visits all the nodes at the same level before moving to the next level.

On the other hand, DFS algorithm starts from the root node (or any other selected node) and explores as far as possible along each branch before backtracking.

Supervised learning involves training a model with labeled data, where both input features and output labels are provided. The model learns the relationship between inputs and outputs to make predictions for unseen data. Common supervised learning tasks include classification and regression.

Unsupervised learning, on the other hand, uses unlabeled data where only input features are provided. The model seeks to discover hidden structures or patterns in the data, such as clusters or data representations. Common unsupervised learning tasks include clustering, dimensionality reduction, and anomaly detection.

Text extraction is the process of extracting text from images or other sources. This can be done with OCR (optical character recognition) or by converting the text to a format that can be read by a text-to-speech system.

Here are some disadvantages of using linear models -

1. They can be biased if the data used to train the model is not representative of the real world.
2. Linear models can also be overfit if the data used to train the model is too small.
3. Linear models assume a linear relationship between the input features and the output variable, which may not hold in reality. This can lead to poor predictions and decreased model performance.

Artificial intelligence interview questions like this can be easy and difficult at the same time as you may know the answers but not on the tip of your tongue. Hence, a quick refresher can help a lot. Reducing dimensionality refers to the reduction of the number of random variables. This can be achieved by different techniques including principal component analysis, low variance filter, missing values ratio, high correlation filter, random forest, and others.

This is a popular AI interview question. A cost function is a scalar function that helps to identify how wrong an AI model is with regard to its ability to determine the relationship between X and Y. In other words, it tells us the neural network’s error factor.

The neural network works better when the cost function is lower. For instance, it takes the output predicted by the neural network and the actual output and then computes how incorrect the model was in its prediction.

So, the cost function will give a lower number if the predictions don’t differ too much from the actual values and vice-versa

The hyper-parameters of ANN are as follows:

Learning rate: It refers to the speed with which the network gets familiar with its parameters

Momentum: This parameter enables coming out of the local minima and smoothening jumps during gradient descent

The number of epochs: This parameter refers to the number of times the whole training dataset is fed to the network during training. One must increase the number of epochs until a decrease in validation accuracy is noticed, even if there is an increase in training accuracy, which is called overfitting.

Number of hidden layers: This parameter specifies the number of layers between the input and output layers.

Number of neurons in each hidden layer: This parameter specifies the number of neurons in each hidden layer.

Activation functions: Activation functions are responsible for determining a neuron's output based on the weighted sum of its inputs. Widely used activation functions include Sigmoid, ReLU, Tanh, and others.

Intermediate tensors are temporary data structures in a computational graph that store intermediate results when executing a series of operations in Artificial Intelligence, particularly in deep learning frameworks. These tensors represent the values produced during the forward pass of a neural network while processing input data before reaching the final output.

Yes, sessions have a lifetime, which starts when the session is created and ends when the session is closed or the script is terminated. In TensorFlow 1.x, sessions were used to execute and manage operations in a computational graph. A session allowed the allocation of memory for tensor values and held necessary resources to execute the operations. In TensorFlow 2.x, sessions and computational graphs have been replaced with a more dynamic and eager execution approach, allowing for simpler and more Pythonic code.

Exploding variables are a phenomenon in which the magnitude of a variable grows rapidly over time, often leading to numerical instability and overflow errors. This can happen when a variable is repeatedly multiplied or divided by a value that is greater than 1 or less than -1. As a result, the variable's value grows exponentially or collapses to zero, causing computational problems.

Linear regression is a basic tool in statistical learning, but it cannot be used to build a deep learning model. Deep learning models require non-linear functions to learn complex patterns in data.

Hyperparameters are parameters that are not learned by the model. They are set by the user and used to control the model's behavior.

An Artificial Super Intelligence system is not one that has been achieved yet. Also known as Super AI, it is a hypothetical system that can surpass human intelligence and execute any task better than a human. The concept of ASI suggests that such an AI can exceed all human intelligence. It can even take complex decisions in harsh conditions and think just like a human would, or even better, develop emotional, sensible relationships.

Overfitting occurs when a model learns the training data too well, including capturing noise and random fluctuations. This often results in a model that performs poorly on unseen or validation data. Techniques to prevent overfitting include:

• Regularization (L1 or L2)
• Early stopping
• Cross-validation
• Using more training data
• Reducing model complexity

Pipelines are used in information extraction to sequentially apply a series of processing steps to input data. This allows for efficient data processing and helps avoid errors.

Full listing hypothesis states that all possible values of a variable should be listed in the data dictionary. Minimum redundancy hypothesis states that all values of a variable should be listed in the data dictionary, but that only the most important values should be listed multiple times.

The gradient descent algorithm helps in optimization and in finding coefficients of parameters that help minimize the cost function. The steps that help achieve this are as follows:

Step 1: Give weights (x,y) random values and then compute the error, also called Sum of Squares Error (SSE).

Step 2: Compute the gradient or the change in SSE when you change the value of the weights (x,y) by a small amount. This step helps us identify the direction in which we must move x and y to minimize SSE.

Step 3: Adjust the weights with the gradients for achieving optimal values for the minimal SSE.

Step 4: Change the weights for predicting and calculating the new error. Step 5: Repeat steps 2 and 3 till the time making more adjustments stops producing significant error reduction.

These types of artificial intelligence interview questions help hiring managers properly guage a candidate’s expertise in this domain. Hence, you must thoroughly understand such questions and enlist all steps properly to move ahead.

There are a number of ways to handle an imbalanced dataset, such as using different algorithms, weighting the classes, or oversampling the minority class.

Algorithm selection: Some algorithms are better suited to handle imbalanced data than others. For example, decision trees and random forests tend to work well on imbalanced data, while algorithms like logistic regression or support vector machines may struggle.

Class weighting: By assigning higher weights to the minority class, you can make the algorithm give more importance to it during training. This can help prevent the algorithm from always predicting the majority class.

Oversampling: You can create synthetic samples of the minority class by randomly duplicating existing samples or generating new samples based on the existing ones. This can balance the class distribution and help the algorithm learn more about the minority class.

The vanishing gradient problem is a difficulty encountered when training artificial neural networks using gradient-based learning methods. This problem is resolved by replacing the activation function of the network. You can use the Long Short-Term Memory (LSTM) network to solve the problem.

It has three gates called input, forgets, and output gates. Here forget gates constantly observe what information needs to be dropped going through the network. In this way, we have short and long-term memory. So, we can transfer the information through the network and retrieve it even at the last stage to identify the context of prediction.

Sigmoid and softmax functions are used in classification problems. Sigmoid maps values to a range of 0-1, which is useful for binary classification problems. Softmax maps values to a range of 0-1 and also ensures that all values sum to 1, which is useful for multi-class classification problems.

Pragmatic analysis is a process of analyzing text data in order to determine the speaker's intention. This is useful in many applications, such as customer service and market research. Here, the main focus is always on what was said to reconsider what is intentionally driving the various aspects of language that require real-world knowledge. It helps you to discover this intentional effect by applying a set of rules that characterize cooperative dialogues. Basically, it means abstracting the meaningful use of language in situations.

Collaborative filtering is a method of making recommendations based on the likes and dislikes of a group of people, while Content-based filtering is a method of making recommendations based on the similarity of the content.

Parsing is the process of breaking down a string of text into smaller pieces, or tokens. This can be done using a regex, or a more sophisticated tool like a parser combinator. There are various techniques for parsing in NLP, including rule-based approaches, statistical approaches, and machine learning-based approaches. Some common parsing algorithms include the Earley parser, the CYK parser, and the chart parser. These algorithms use various methods such as probability models, tree-based representations, and context-free grammars to parse a text and identify its grammatical structure.

A human brain learns from its experiences or from the past experiences it has in its memory. Just like the human brain, Limited Memory Artificial Intelligence learns from past data already in the memory and makes decisions on their behalf. But this data is stored for some specific time, and they cannot add it to their information center. Self-Driving is one of the best technology examples of Limited Memory AI. Self Driving cars can store data during driving, like how many vehicles are moving around them, vehicle speed, and the traffic lights. From their experiences, they understand how to drive properly on the road in heavy and moderate traffic. Few companies are focused on these types of technologies.

Stochastic gradient descent (SGD) updates the model's weights using the gradient calculated from a single training example. It converges faster because of frequent weight updates; however, it can have a noisy convergence due to high variance in gradients.

Mini-batch gradient descent calculates the gradient using a small batch of training examples. It strikes a balance between the computational efficiency of batch gradient descent and the faster convergence of SGD. The noise in weight updates is reduced, leading to a more stable convergence.

Data standardization is a technique that is mostly performed as a preprocessing step of developing ML models to formalize the range of features of an input data set.

Understanding data: You need to understand the distribution of your data to decide which standardization technique is appropriate. For example, if the data is normally distributed, you can use z-score normalization.

Choosing standardization technique: Standardization techniques such as z-score normalization, min-max scaling, and mean normalization can be used depending on the type of data.

Implementation: Standardizing data can be implemented using programming languages such as Python, and R, or tools such as Excel or a data automation platform.

Impact on model performance: Standardization can significantly impact the performance of machine learning models. Hence, it's important to standardize the data before feeding it into the model

Here's a basic implementation of Naïve Bayes Classifier in Python using the scikit-learn library. This example demonstrates the process of loading a dataset, splitting it into training and testing sets, fitting the model, and calculating its accuracy.

Entropy is unpredictability in the data; the more uncertainty, the higher the entropy will be. Entropy is used by information gain to make decisions. If the entropy is fewer, the information will be big.

Information gain is used in random forests and decision trees to decide the best split. Thus, the bigger the information gain, the better the split and the shorter the entropy.
The entropy is used to calculate the information gain of a dataset before and after a split.

Entropy is the calculation of the probability of suspense in the data. The main purpose is to reduce entropy and increase information gain. The feature having the maximum information is considered essential by the algorithm and is used for training the model.

Here's a basic implementation of the Random Forest Regressor in Python using the scikit-learn library. This example demonstrates the process of loading a dataset, splitting it into training and testing sets, fitting the model, and calculating the predictions.

Kernel tricks are a technique used in Artificial Intelligence, particularly in machine learning algorithms, to transform a non-linearly separable problem into a linearly separable one. They are commonly used in Support Vector Machines (SVMs) and other kernel-based algorithms for solving complex classification or regression tasks.

The main idea behind kernel tricks is to map the input data from a lower-dimensional space to a higher-dimensional space, in which the data points become linearly separable. This mapping is done using a mathematical function called the kernel function.

Here's a basic implementation of the K-Nearest Neighbors (KNN) algorithm in Python using the scikit-learn library. This example demonstrates the process of loading a dataset, splitting it into training and testing sets, fitting the model, and calculating its accuracy.

The Gini coefficient formula is as follows:

Here are a few steps using which you can calculate the Gini coefficient:

Organize the data into a table with the category head mentioned below

All the rows must organize from the poorest to the richest.
Fill the '% of Population that is richer' column by adding all terms in 'Fraction of Population' below that row.
Calculate the Score for each of the rows. The formula for the Score is:
Score = Fraction of Income * (Fraction of Population + 2 * % of Population that is richer).
Next, add all the terms in the ‘Score’ column. Let us call it ‘Sum.’
Using the formula calculate the Gini coefficient: = 1 –Sum.