Artificial Intelligence Interview Questions and Answers for 2025

It's no surprise that this one pops up often in artificial intelligence interview questions. The cost function is used to measure the performance of an AI model by computing the error between predicted and expected values. The gradient descent helps minimize this cost function by tuning the model parameters. Not every solution has a coinciding local and global minimum. There are cases where we have multiple local minimums, but there is always only one global minimum. The cost function is the minimum at the global minimum. However, it is not always possible to reach the global minimum, so the idea is to reach as much as close to it. This is controlled by the learning rate. The batch, stochastic, and mini-batch gradient descent are different techniques of gradient descent to reach a solution close to the optimal. 

At each stage, Batch Gradient Descent employs the complete set of training data. Its performance on exceptionally large training sets is, therefore, terrible. As a result, it is not preferred as we usually deal with Big Data regarding AI solutions. Every time a step is completed, stochastic gradient descent simply chooses a random instance from the training set and computes the gradients based solely on that one occurrence. This obviously speeds up the process because there is a lot fewer data for it to deal with at each iteration. Since only one instance must be kept in memory during each iteration, it also allows for training large training sets. Mini-batch gradient descent computes the gradients on small random groups of examples termed mini batches rather than computing the gradients based on the entire training set as in batch gradient descent or based on only one instance as in stochastic gradient descent. Performance can be improved, which is the fundamental benefit of mini-batch gradient descent over stochastic gradient descent. 

There are a lot of hyperparameters in a neural network that one can tweak. We can change the type of activation function, the number of hidden layers, the number of neurons in a hidden layer, weight initializations, etc. To start with, we can begin with a single or two hidden layers. A deep neural network contains more hidden layers. We can then increase the number of neurons in each hidden layer before trying to add another layer to the architecture. A common practice is to size them to form a funnel, with fewer and fewer neurons at each layer. Finding a perfect number of hidden layers and neurons in each layer is a difficult job and does require tuning. A simpler approach is to pick a model with more layers and neurons than you need, then use early stopping to prevent it from overfitting. However, we can use grid search with cross-validation to find the right hyperparameters.

The decision tree is one of the most basic techniques used today for data classification. It is a part of tree neural networks. We employ a decision-tree-like model that includes every potential outcome. Numerous nodes make up a decision tree. The Al system uses a test at each node to move the query down a node. Up until the query is sent to the terminal or leaf node, this process continues. There is a preassigned value for each leaf node. This value is regarded as the tree's output. This tree can alternatively be seen as a flowchart with the root node at the top and the decision-making root node at the bottom.

The following are some of the terms frequently associated with decision trees: 

• Root Node – It can be found at the top or the start of a tree. It refers to all the analyzed data. Based on the characteristics of the data, the flow from the root node to the other nodes is determined.  
• Splitting - A node gets split into two or more sub-nodes during the splitting process.  
• Decision Node - The Decision or Interior Node is where splitting happens. A sub-node is further subdivided into more sub-nodes in this location. 
• Leaf Node - The last node is called Leaf Node. There are no sub-nodes in this node. 
• Branch or Sub-Tree - A decision tree's subdivisions are called branches or subtrees. 
• Parent Node and Child Node - The nodes existing below the current nodes are known as the child or sub-nodes. The nodes that exist above a node are known as the parent nodes. 

In the process of Text Normalizations, we undertake several steps to normalize the text to a lower level from multiple documents. The textual data from all the documents altogether is called a corpus. The steps involved in text normalization are – 

• Sentence Tokenization 

Sentence tokenization is also known as sentence segmentation. In this step, a string of written language is divided into its component sentences. In human languages, we can split sentences into further smaller sentences whenever we see a punctuation mark. For example, the sentence “This is amazing! I won the competition.” Will be tokenized into two sentences – “This is amazing!” and “I won the competition.” 

• Word Tokenization 

Word tokenization is also known as word segmentation. In this process, each sentence is further divided into component words or tokens. In English and many other languages that are based on Latin alphabets, space is a good estimate of a word divider. For example, “interview questions artificial intelligence” when tokenized produces [‘interview,’ ‘questions,’ ‘artificial,’ ‘intelligence’]. 

• Text Lemmatization and Stemming 

For grammar rules, documents can have various forms of a word, such as run, runs, and running. Moreover, sometimes, we have related words with similar meanings, such as nation, national, and nationality. Two of the specialized cases of normalization are Stemming and lemmatization. However, they are different from each other. 

Stemming refers to a crude heuristic process that involves chopping off the ends of the words with the aim of achieving this goal correctly. Stemming algorithms work by cutting off the common prefixes or suffixes that can be found in a word. This cutting does not always produce a successful result, i.e., a meaningful word is not always obtained after stemming. For example, the stemming operation for collected will be collected, but for closed will be closed. 

Lemmatization refers to performing tasks properly with vocabulary and morphological analysis of words. It is done with the aim of removing inflectional endings only and obtaining the base or dictionary form of a word, known as the lemma. For example, the stemming operation for collected will be collected, and for closed will be closed. 

Bag of Words: 

Machine learning algorithms cannot perform with raw text. The text is first converted into vectors of numbers. This process is known as feature extraction. The bag of words is an extremely popular model which uses a feature extraction technique to work with the text. It explains the occurrence of each word within a document. We can use this model by designing a vocabulary of known words (also called tokens) and choosing a measure of the presence of the known words. Any information about the order or structure of words is rejected. That is why we call it a bag of words. This model attempts to find if a known word occurs in a document, but it does not know the location of the word in the document. The perception is that similar documents have similar content. We can also learn something about the meaning of the document from its content page. 

To implement a bag of words algorithm, we must follow the following steps: 

• Text Normalization: Collection of data and pre-processing of it 
• Creating Dictionary: Listing out all the unique words occurring in the corpus. (Vocabulary) 
• Creating document vectors: For each document in the corpus, find out the frequency of each word from the unique list of words. 
• Creating document vectors for all the documents. 

Term Frequency and Inverse Document Frequency (TFIDF) 

Term Frequency and Inverse Document Frequency (TFIDF) is a statistical measure that evaluates the importance of a word to a document in a corpus. The TFIDF scoring value increases in proportion to the number of times a word appears in the document, but it is equalized by the number of documents in the corpus that contains the word. 

The following formula is used to calculate a TF-IDF score for a given term x within document y. 

A staple in AI Interview Questions, be prepared to answer this one. The most common and early activation functions include: 

• Identity function – It is a linear activation function where the output remains the same as the input.

f(x)=x

• Binary step function – It is most widely used in single-layer artificial networks to convert the net input to a binary output representing 1s and 0s. Here, the value θ represents the threshold. If the value of x is greater than the threshold, then the activation function outputs 1, else 0. 

Computer Vision is a domain of AI that gains information from digital images and videos. It helps to understand and automate tasks that the human visual system can do. The several applications of Computer Vision include: 

• Face Recognition - Identifying and tagging faces in images and videos like Google Photos and Facebook. 
• Content-based Image retrieval (CBIR) - Identifying images based on their composition, color, texture, etc. CBIR is used in Google Lens, CT, or MRI image databases, etc. 
• Autonomous vehicles (self-driving cars) - The development of autonomous vehicles was aided using computer vision, which is used to recognize and categorize items like traffic signals or road signs, produce 3D maps, or estimate motion. 
• Healthcare - Computer vision can be successfully used in the context of medical X-ray imaging to treat and research, recreate MRI images, plan surgeries, find abnormalities and changes by comparing cancerous and non-cancerous cells, and identify the type of pathology in images, etc. 
• Object Character Recognition (OCR) – OCR helps to extract the text from documents, images, etc., to a machine-coded text or digital text. It can be used in reading documents, answer sheets, etc. 

The idea of conditional probability considers data on the likelihood of one event occurring while estimating the likelihood of another event. This idea can be expanded upon to calculate the likelihood that a certain effect was caused by a particular cause and to adjust probabilities in the context of new knowledge. The Bayes theorem describes the process for updating this probability. The Bayesian network is based on the Bayes theorem and may be used to respond to probabilistic queries in AI. Because they are constructed from a probability distribution and employ probability theory for anomaly detection and prediction, Bayesian networks are probabilistic. Graphical representations of the probability link between a set of variables are called Bayesian networks. There are several edges in this directed cycle graph, and each edge denotes a conditional reliance. Document classification, semantic search, spam filter, image processing, etc., are some of the applications of a Bayesian network.

Overfitting is the term used to describe a model that performs well on training data but not well on test data. Overfitting occurs when the model is excessively complicated in comparison to the volume and granularity of the training data. The following options may be used to address the issue: 

• Simplify the model by choosing one with fewer parameters. For example, using a linear model rather than a high-degree polynomial model. 
• Reduce the number of attributes in the training data. 
• Constraining the model hyperparameters by using regularization. 
• Collect more training data. 
• To make the training data less noisy by fixing data errors, removing outliers, etc. 

Underfitting normally occurs when your model is too basic to understand the fundamental structure of the data. The principal solutions to this issue are: 

• Choosing a more robust model with more variables. 
• Doing robust feature engineering to increase the quality of the features fed to the learning algorithm. 

A data pipeline is a series of components used in the data processing. Usually, components operate asynchronously. Each component in the pipeline takes in a significant quantity of data, processes it, and outputs the result in a different data store. Later, the following component in the pipeline takes this data and produces its own output, and so on. Each component is mostly self-contained; the data store acts as the only contact between them. This makes the system easy to understand, and several teams can concentrate on various components. Additionally, if a component malfunctions, the downstream components can frequently keep operating normally by using its last output.

An autoencoder is a feed-forward neural network with three layers. They are unsupervised machine learning models that recreate input data to minimize its size. They have one characteristic in that the autoencoder's only job is to replicate the inputs since the targets are the same values as the inputs. This implies that the input layer and output layer must each include the same number of neurons. These models are taught as supervised machine learning models and then operate as unsupervised models during inference. Encoder and decoder are the two components of an autoencoder. The encoder performs the function of a compression unit by condensing the input data. The decoder reconstructs the compressed input after decompression.

The outcome of multiplying a number between 0 and 1 with another number in the same range is a significantly smaller number than either of the two. The backpropagation technique propagates the error gradient while moving from the output layer to the input layer. Backpropagation multiplies the derivatives with values between 0 and 1, acting like a chain rule. We had to multiply more than 0 by 1 number because of the addition of layers, which required us to backpropagate, and this usually makes the weights exceedingly small very rapidly. For activation functions to work, the signal must have large enough values. The farther neuron layers are from the output, the higher the likelihood that they’ll get locked out of updates because the signals are too small, and the activation functions will stop them. In artificial neural networks, this phenomenon refers to as the vanishing gradient problem. The deep neural networks are affected by the vanishing gradient problem, which makes it difficult to train the lower layers. As a result, your network either stops learning altogether or learns extremely slowly.

In a vanishing gradient, the weights of the links grow extremely small, thereby not allowing the network to learn further. The exploding gradient problem is the opposite. It is the case in which the gradients can grow bigger and bigger. The weights are so large that when the neuron's output is updated using these weights, the algorithm diverges. This phenomenon is most likely to happen in recurrent neural networks where there is a presence of memory and feedback loops.

A must-know for anyone heading into an AI interview, this question is frequently asked in AI Interview Questions. LSTM, or Long Short-Term Memory, is a complex, black-box model that belongs to the family of recurrent neural networks. LSTM has feedback connections, i.e., it can process the entire sequence of data, apart from single data points such as images. LSTM introduces memory units known as cell states. Gates in LSTMs control the addition and removal of information from the cell state. These gates may allow information to enter and exit the cell. It has a sigmoid neural network layer and a pointwise multiplication operation that helps the mechanism. The network can interact with the memory cells only via the gates.

A component of bagging ensemble models is random forest. In an ensemble model, the estimation is produced by using two or more predictors. A group of decision tree classifiers, for instance, could be trained on various random subsets of the training set. We just gather the predictions from each individual tree and then predict the class that receives the most votes. A Random Forest is a group of such decision trees. It is one of the most powerful classification algorithms. The random forest algorithm shares the hyper-parameters of a decision tree. The algorithm can handle large datasets and does not require much data preparation or numeric data scaling.

The Hopfield network is an interconnected single-layer feedforward network. It is auto-associative, which means it can create associations from its own content. The auto-associate memory allows the Hopfield network to recover an original stored vector. The inputs to the network can be binary or bipolar. The network has symmetric weights and no self-connection. The input neurons, after the updating process, reach the output layer. The output is then again fed to the input acting as a feedback connection. The outputs are fed back to the input of other processing elements but not to themselves. This process continues until there are no latest updates happening to the network responses. The connections are positive if there is no change in the output and the input processing elements. Various optimization problems utilize the associative Hopfield network. Hopfield networks can further be classified as discrete and continuous Hopfield networks.

Kohonen self-organizing feature map is an artificial neural network that follows an unsupervised learning approach. It consists of a network performing the mapping of a wide pattern space into a typical feature space. That means it converts the patterns of arbitrary dimensionality into a response of a one-dimensional or two-dimensional array. The maps help to maintain the relationship among the input patterns in their arbitrary dimension. A one-dimensional array consists of a single layer where each of the neurons relates to the other neurons in the layer. A two-dimensional array means that there are two layers of fully connected neurons.

In ensemble learning, the terms "hard voting classifiers" and "soft voting classifiers" describe a chain of weak classifiers that produce an inference. The classifier is referred to as a hard-voting classifier or a majority-vote classifier if we combine the predictions of each classifier and predict the class that receives the greatest number of votes. You can predict the class with the highest-class probability, averaging over all the individual classifiers if all classifiers are able to estimate class probabilities. This is termed the soft voting classifier. Because it lends more weight to votes with elevated levels of confidence, it frequently produces results that are higher than hard voting.

A Reinforcement Learning agent's performance can be evaluated by adding up the rewards it receives. You can run numerous episodes and examine the average total rewards with acceptable standard deviation in a simulated setting.

The process of backpropagation is used to train artificial neural networks. Backpropagation is the term used to describe the entire process of training an artificial neural network utilizing the backpropagation step, each of which performs a gradient descent step while computing the gradients. Reverse-mode autodiff, on the other hand, is only an efficient gradient computation method that is employed by backpropagation.

Whether or not a year’s data is sufficient can only be answered by looking at the volume of data which will be primarily driven by the number of users accessing the portal. This is a case of NLP. In such cases, it is always better to go ahead with pre-trained models and avoid modeling from scratch. There are a lot of open-source pre-trained models available such as BERT, MLNet, etc. We can always test our samples using these pre-trained models. It also has provision for incremental training or transfer learning which can let us feed our data to the model for training purposes. This can prove beneficial if we are working on a particular use case and leverage the existing pre-trained models trained on thousands or millions of data points.

Overfitting is possible while working with the random forest since tree-based models are free to learn. Our models can be controlled using hyperparameters. There are certain hyperparameters of random forest that need to be set so that the model is constrained from over-learning the patterns in the data.

A voting ensemble, which frequently produces even better results, can be created if you have trained five separate models and they have all achieved a 75% precision rate. It functions better if the model’s learning algorithms are quite different from one another. The goal of bagging and pasting ensembles is to train them on different training instances, and however even if they aren't, it still works if the models are dissimilar.

Reduce the learning rate if your Gradient Boosting ensemble overfits the training set. Additionally, early stopping could be used to determine the ideal number of predictors. Early stopping is a type of regularization used when training a learner using an iterative approach, such as gradient descent, to prevent overfitting.

The decision tree is a tree-based algorithm that requires little to no data preparation. It is not a distance-based algorithm; therefore, scaling does not have any impact on it. Underfitting might be caused due to stricter constraints on the hyperparameters of the model, less data records, etc.

The time taken by an algorithm or a model to learn depends on the computational complexity of the model training. The complexity of a decision tree is O (n x m x log(m)). Therefore, if we multiply the number of records by ten, the training time will multiply by 11. We can expect the newer model to get trained in 495 minutes.

If you translate a statement word-by-word, the outcome will be awful. When translated word by word, the Spanish phrase "me quedo en la India" (which translates to "I stay in India") reads, "I stay in India." It is much preferable to read the entire text before translating it. While an encoder-decoder RNN will first read the entire sentence before translating it, a basic sequence-to-sequence RNN will begin translating a sentence as soon as the first word is read.

If you want to train a classifier but only have a small amount of labeled data, you could train a deep autoencoder on the entire dataset consisting of labeled and unlabeled records. Then reuse its lower half for the classifier and train the classifier using the labeled data. When training the classifier, you should freeze the reused layers if there is not much labeled data available.

One architecture for categorizing videos based on their visual content might be to take one frame per second, process each frame through a convolutional neural network, feed the CNN's output to a sequence-to-vector RNN, and then pass the RNN's output through a SoftMax layer to provide all the class probabilities. Cross entropy would be the sole cost function used for training. You could convert each second of audio to a spectrograph, feed the output of the spectrograph to a CNN, and then use the output of the CNN to train an RNN if you also want to use the audio for categorization.

Here, it is a type of unsupervised learning since we do not have any labels associated with the data. We must create clusters that are a part of the clustering model. We can group these customers based on their similarities, like similar age groups, shopping behavior, etc. A clustering model will try to identify a pattern in the data. Data clustering methods look for clusters in the data, and there are many ways to adjust and modify these algorithms. The idea behind clustering is that objects in a cluster must be related to one another. Related elements are placed next to one another in the data grouping. While there are so many clustering algorithms present, the K-means clustering algorithm might be the best place to start with. To decide the appropriate number of clusters, we can use silhouette analysis or elbow method. However, we can initially use a rule of thumb to include the root of half of the number of data points as the number of clusters in our algorithm.

A CNN contains far less parameters than a fully connected DNN, which speeds up training, lowers the chance of overfitting, and necessitates much less training data. This is because successive layers are only partially connected and because it heavily reuses their weight. CNNs can generalize significantly better than DNNs for image processing tasks like classification, utilizing less training data. This is because CNNs use kernels.

When using distributed TensorFlow, the model parameters continue to exist on the cluster even after the session, and client software is closed since the variable values are stored in containers that are managed by the cluster. You only need to start a new cluster session and save the model.

Yes, dropouts do slow down training by a ratio of two. Since it is only activated during training, it has no effect on inference.

We only need one neuron in the output layer of a neural network to categorize email as spam or junk, for example, by signaling the likelihood that the email is spam. When estimating a probability, you would normally utilize the logistic activation function at the output layer. It gives the probability of an event occurring.

An autoencoder's ability to completely rebuild its inputs does not necessarily indicate that it is a good autoencoder; it could simply be an overcomplete autoencoder that has learned the technique of copying its inputs to the coding layer before applying them to the outputs. If an autoencoder result in incredibly poor reconstructions, it cannot be said to be good. Measuring the reconstruction loss using a tool like MSE is one method for evaluating an autoencoder's performance.