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Ever since we realized how Artificial Intelligence is positively impacting the market, nearly every large business is on the lookout for AI professionals to help them make their vision a reality. In this Artificial Intelligence Interview Questions blog, I have collected the most frequently asked questions by interviewers. These questions are collected after consulting with Machine Learning Course.
In case you have attended any Artificial Intelligence interview in the recent past, do paste those interview questions in the comments section and we’ll answer them at the earliest. You can also comment below if you have any questions in your mind, which you might face in your Artificial Intelligence interview.
This Edureka Artificial Intelligence (AI) Interview Questions and Answers will help you to clear the Artificial Intelligence (AI) Interviews.
In this blog on Artificial Intelligence Interview Questions, I will be discussing the top Artificial Intelligence related questions asked in your interviews. So, for your better understanding I have divided this blog into the following 3 sections:
Artificial Intelligence | Machine Learning | Deep Learning |
Originated in 1950s | Originated in 1960s | Originated in 1970s |
AI represents simulated intelligence in machine | ML is the practice of getting machines to make decisions without being programmed | DL is the process of using Artificial Neural Networks to solve complex problems |
AI is a subset of Data Science | ML is a subset of AI & Data Science | DL is a subset of ML, AI & Data Science |
Aims to build machines which are capable of thinking like humans | Aims to build machines learning through data so that they can solve problems | Aims to build neural networks that automatically discover patterns for feature detection |
Artificial Intelligence vs Machine Learning vs Deep Learning – Artificial Intelligence Interview Questions – Edureka
Google’s Search Engine – Artificial Intelligence Interview Questions – Edureka
Google’s Search Engine
One of the most popular AI Applications is the google search engine. If you open up your chrome browser and start typing something, Google immediately provides recommendations for you to choose from. The logic behind the search engine is Artificial Intelligence.
AI uses predictive analytics, NLP and Machine Learning to recommend relevant searches to you. These recommendations are based on data that Google collects about you, such as your search history, location, age, etc. Thus, Google makes use of AI, to predict what you might be looking for.
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Domains Of AI – Artificial Intelligence Interview Questions – Edureka
Artificial Intelligence is a technique that enables machines to mimic human behavior. Whereas, Machine Learning is a subset of Artificial Intelligence. It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so.
Therefore Machine Learning is a technique used to implement Artificial Intelligence.
Artificial Intelligence vs Machine Learning – Artificial Intelligence Interview Questions – Edureka
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Types Of Machine Learning – Artificial Intelligence Interview Questions – Edureka
The Q-learning is a Reinforcement Learning algorithm in which an agent tries to learn the optimal policy from its past experiences with the environment. The past experiences of an agent are a sequence of state-action-rewards:
What Is Q-Learning? – Artificial Intelligence Interview Questions – Edureka
In the above state diagram, the Agent(a0) was in State (s0) and on performing an Action (a0), which resulted in receiving a Reward (r1) and thus being updated to State (s1).
Deep learning imitates the way our brain works i.e. it learns from experiences. It uses the concepts of neural networks to solve complex problems.
What Is Deep Learning? – Artificial Intelligence Interview Questions – Edureka
Any Deep neural network will consist of three types of layers:
Biological Neurons – Artificial Intelligence Interview Questions – Edureka
Deep Neural Network – Artificial Intelligence Interview Questions – Edureka
Feedforward Neural Network
Recurrent Neural Network(RNN) – Long Short Term Memory
Q11. What are Bayesian Networks?
On the occurrence of an event, Bayesian Networks can be used to predict the likelihood that any one of several possible known causes was the contributing factor.
Bayesian Network – Artificial Intelligence Interview Questions – Edureka
For example, a Bayesian network could be used to study the relationship between diseases and symptoms. Given various symptoms, the Bayesian network is ideal for computing the probabilities of the presence of various diseases.
In artificial intelligence (AI), a Turing Test is a method of inquiry for determining whether or not a computer is capable of thinking like a human being.
AI Turing Test – Artificial Intelligence Interview Questions – Edureka
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Generally, a Reinforcement Learning (RL) system is comprised of two main components:
Reinforcement Learning – Artificial Intelligence Interview Questions – Edureka
To understand this better, let’s suppose that our agent is learning to play counterstrike. The RL process can be broken down into the below steps:
Counter-Strike Example – Artificial Intelligence Interview Questions – Edureka
To learn more about Reinforcement Learning you can go through this video recorded by our Machine Learning experts.
Markov’s Decision Process – Artificial Intelligence Interview Questions – Edureka
To briefly sum it up, the agent must take an action (A) to transition from the start state to the end state (S). While doing so, the agent receives rewards (R) for each action he takes. The series of actions taken by the agent, define the policy (π) and the rewards collected define the value (V). The main goal here is to maximize rewards by choosing the optimum policy.
To better understand the MDP, let’s solve the Shortest Path Problem using the MDP approach:
Shortest Path Problem – Artificial Intelligence Interview Questions – Edureka
Given the above representation, our goal here is to find the shortest path between ‘A’ and ‘D’. Each edge has a number linked with it, this denotes the cost to traverse that edge. Now, the task at hand is to traverse from point ‘A’ to ‘D’, with minimum possible cost.
In this problem,
You start off at node A and take baby steps to your destination. Initially, only the next possible node is visible to you, thus you randomly start off and then learn as you traverse through the network. The main goal is to choose the path with the lowest cost.
Since this is a very simple problem, I will leave it for you to solve. Make sure you mention the answer in the comment section.
The RL agent works based on the theory of reward maximization. This is exactly why the RL agent must be trained in such a way that, he takes the best action so that the reward is maximum.
The collective rewards at a particular time with the respective action is written as:
Reward Maximization Equation – Artificial Intelligence Interview Questions – Edureka
The above equation is an ideal representation of rewards. Generally, things don’t work out like this while summing up the cumulative rewards.
Reward Maximization – Artificial Intelligence Interview Questions – Edureka
Let me explain this with a small game. In the figure you can see a fox, some meat and a tiger.
The next thing to understand is, how discounting of rewards work?
To do this, we define a discount rate called gamma. The value of gamma is between 0 and 1. The smaller the gamma, the larger the discount and vice versa.
So, our cumulative discounted rewards is:
Reward Maximization with Discount Equation – Artificial Intelligence Interview Questions – Edureka
An important concept in reinforcement learning is the exploration and exploitation trade-off.
Exploration, like the name suggests, is about exploring and capturing more information about an environment. On the other hand, exploitation is about using the already known exploited information to heighten the rewards.
Exploitation & Exploration – Artificial Intelligence Interview Questions – Edureka
Parametric vs Non Parametric model – Artificial Intelligence Interview Questions – Edureka
Model Parameters vs Hyperparameters – Artificial Intelligence Interview Questions – Edureka
Grid Search
Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques.
Random Search
It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.
Bayesian Optimization
This includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called surrogate model (Gaussian Process). Bayesian Optimization uses Gaussian Process (GP) function to get posterior functions to make predictions based on prior functions.
Cross-validation: The idea behind cross-validation is to split the training data in order to generate multiple mini train-test splits. These splits can then be used to tune your model.
More training data: Feeding more data to the machine learning model can help in better analysis and classification. However, this does not always work.
Remove features: Many times, the data set contains irrelevant features or predictor variables that are not needed for analysis. Such features only increase the complexity of the model, thus leading to possibilities of data overfitting. Therefore, such redundant variables must be removed.
Early stopping: A machine learning model is trained iteratively, this allows us to check how well each iteration of the model performs. But after a certain number of iterations, the model’s performance starts to saturate. Further training will result in overfitting, thus one must know where to stop the training. This can be achieved by a mechanism called early stopping.
Regularization: Regularization can be done in n number of ways, the method will depend on the type of learner you’re implementing. For example, pruning is performed on decision trees, the dropout technique is used on neural networks and parameter tuning can also be applied to solve overfitting issues.
Use Ensemble models: Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. This is one of the best ways to prevent overfitting. An example is Random Forest, it uses an ensemble of decision trees to make more accurate predictions and to avoid overfitting.
Dropout is a type of regularization technique used to avoid overfitting in a neural network. It is a technique where randomly selected neurons are dropped during training.
Dropout – Artificial Intelligence Interview Questions – Edureka
The Dropout value of a network must be chosen wisely. A value too low will result in a minimal effect and a value too high results in under-learning by the network.
Text Mining vs NLP – Artificial Intelligence Interview Questions – Edureka
Components Of NLP – Artificial Intelligence Interview Questions – Edureka
Natural Language Understanding includes:
Natural Language Generation includes:
Stemming algorithms work by cutting off the end or the beginning of the word, taking into account a list of common prefixes and suffixes that can be found in an inflected word. This indiscriminate cutting can be successful on some occasions, but not always.
Stemming – Artificial Intelligence Interview Questions – Edureka
Lemmatization, on the other hand, takes into consideration the morphological analysis of the words. To do so, it is necessary to have detailed dictionaries which the algorithm can look through to link the form back to its lemma.
Fuzzy Logic Architecture – Artificial Intelligence Interview Questions – Edureka
Expert Systems – Artificial Intelligence Interview Questions – Edureka
Computer Vision is a field of Artificial Intelligence that is used to obtain information from images or multi-dimensional data. Machine Learning algorithms such as K-means is used for Image Segmentation, Support Vector Machine is used for Image Classification and so on.
Computer Vision And AI – Artificial Intelligence Interview Questions – Edureka
Therefore Computer Vision makes use of AI technologies to solve complex problems such as Object Detection, Image Processing, etc.
Therefore, it is better to choose supervised classification for image classification in terms of accuracy.
Image Smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors, this reduces any distortions caused by contrasts.
Image Smoothing – Artificial Intelligence Interview Questions – Edureka
“In the context of artificial intelligence(AI) and deep learning systems, game theory is essential to enable some of the key capabilities required in multi-agent environments in which different AI programs need to interact or compete in order to accomplish a goal.”
Game Theory And AI – Artificial Intelligence Interview Questions – Edureka
Minimax is a recursive algorithm used to select an optimal move for a player assuming that the other player is also playing optimally.
A game can be defined as a search problem with the following components:
There are two players involved in a game:
The following approach is taken for a Tic-Tac-Toe game using the Minimax algorithm:
Step 1: First, generate the entire game tree starting with the current position of the game all the way up to the terminal states.
Tic-Tac-Toe – Artificial Intelligence Interview Questions – Edureka
Step 2: Apply the utility function to get the utility values for all the terminal states.
Step 3: Determine the utilities of the higher nodes with the help of the utilities of the terminal nodes. For instance, in the diagram below, we have the utilities for the terminal states written in the squares.
Tic-Tac-Toe – Artificial Intelligence Interview Questions – Edureka
Let us calculate the utility for the left node(red) of the layer above the terminal:
MIN{3, 5, 10}, i.e. 3.
Therefore, the utility for the red node is 3.
Similarly, for the green node in the same layer:
MIN{2,2}, i.e. 2.
Tic-Tac-Toe – Artificial Intelligence Interview Questions – Edureka
Step 4: Calculate the utility values.
Step 5: Eventually, all the backed-up values reach to the root of the tree. At that point, MAX has to choose the highest value:
i.e. MAX{3,2} which is 3.
Therefore, the best opening move for MAX is the left node(or the red one).
To summarize,
Minimax Decision = MAX{MIN{3,5,10},MIN{2,2}}
= MAX{3,2}
= 3
Alpha-beta Pruning
If we apply alpha-beta pruning to a standard minimax algorithm, it returns the same move as the standard one, but it removes all the nodes that are possibly not affecting the final decision.
Alpha-beta Pruning – Artificial Intelligence Interview Questions – Edureka
In this case,
Minimax Decision = MAX{MIN{3,5,10}, MIN{2,a,b}, MIN{2,7,3}}
= MAX{3,c,2}
= 3
Hint: (MIN{2,a,b} would certainly be less than or equal to 2, i.e., c<=2 and hence MAX{3,c,2} has to be 3.)
Facebook uses DeepFace for face verification. It works on the face verification algorithm, structured by Artificial Intelligence (AI) techniques using neural network models.
Face Verification – Artificial Intelligence Interview Questions – Edureka
Here’s how face verification is done:
Input: Scan a wild form of photos with large complex data. This involves blurry images, images with high intensity and contrast.
Process: In modern face recognition, the process completes in 4 raw steps:
Output: Final result is a face representation, which is derived from a 9-layer deep neural net
Training Data: More than 4 million facial images of more than 4000 people
Result: Facebook can detect whether the two images represent the same person or not
Target Marketing involves breaking a market into segments & concentrating it on a few key segments consisting of the customers whose needs and desires most closely match your product.
It is the key to attracting new business, increasing your sales, and growing the company.
The beauty of target marketing is that by aiming your marketing efforts at specific groups of consumers it makes the promotion, pricing, and distribution of your products and/or services easier and more cost-effective.
Targeted Marketing – Artificial Intelligence Interview Questions – Edureka
Machine Learning in targeted marketing:
Fraud Detection Using AI – Artificial Intelligence Interview Questions – Edureka
The following approach is followed for detecting fraudulent activities:
Data Extraction: At this stage data is either collected through a survey or web scraping is performed. If you’re trying to detect credit card fraud, then information about the customer is collected. This includes transactional, shopping, personal details, etc.
Data Cleaning: At this stage, the redundant data must be removed. Any inconsistencies or missing values may lead to wrongful predictions, therefore such inconsistencies must be dealt with at this step.
Data Exploration & Analysis: This is the most important step in AI. Here you study the relationship between various predictor variables. For example, if a person has spent an unusual sum of money on a particular day, the chances of a fraudulent occurrence are very high. Such patterns must be detected and understood at this stage.
Building a Machine Learning model: There are many machine learning algorithms that can be used for detecting fraud. One such example is Logistic Regression, which is a classification algorithm. It can be used to classify events into 2 classes, namely, fraudulent and non-fraudulent.
Model Evaluation: Here, you basically test the efficiency of the machine learning model. If there is any room for improvement, then parameter tuning is performed. This improves the accuracy of the model.
This problem statement can be solved using the KNN algorithm, that will classify the applicant’s loan request into two classes:
K Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into the target class, depending on the features of its neighboring data points.
Bank Loan Approval Using AI – Artificial Intelligence Interview Questions – Edureka
The following steps can be carried out to predict whether a loan must be approved or not:
Data Extraction: At this stage data is either collected through a survey or web scraping is performed. Data about the customers must be collected. This includes their account balance, credit amount, age, occupation, loan records, etc. By using this data, we can predict whether or not to approve the loan of an applicant.
Data Cleaning: At this stage, the redundant variables must be removed. Some of these variables are not essential in predicting the loan of an applicant, for example, variables such as Telephone, Concurrent credits, etc. Such variables must be removed because they will only increase the complexity of the Machine Learning model.
Data Exploration & Analysis: This is the most important step in AI. Here you study the relationship between various predictor variables. For example, if a person has a history of unpaid loans, then the chances are that he might not get approval on his loan applicant. Such patterns must be detected and understood at this stage.
Building a Machine Learning model: There are n number of machine learning algorithms that can be used for predicting whether an applicant loan request is approved or not. One such example is the K-Nearest Neighbor, which is a classification and a regression algorithm. It will classify the applicant’s loan request into two classes, namely, Approved and Disapproved.
Model Evaluation: Here, you basically test the efficiency of the machine learning model. If there is any room for improvement, then parameter tuning is performed. This improves the accuracy of the model.
To understand spam detection, let’s take the example of Gmail. Gmail makes use of machine learning to filter out such spam messages from our inbox. These spam filters are used to classify emails into two classes, namely spam and non-spam emails.
Let’s understand how spam detection is done using machine learning:
Spam Detection Using AI – Artificial Intelligence Interview Questions – Edureka
This can be done by studying the past data and building a model that shows how the sales have varied over a period of time. Sales Forecasting is one of the most common applications of AI. Linear Regression is one of the best Machine Learning algorithms used for forecasting sales.
When both sales and time have a linear relationship, it is best to use a simple linear regression model.
Linear Regression is a method to predict dependent variable (Y) based on values of independent variables (X). It can be used for the cases where we want to predict some continuous quantity.
In this example, the dependent variable ‘Y’ represents the sales and the independent variable ‘X’ represents the time period. Since the sales vary over a period of time, sales is the dependent variable.
Forecasting Sales Using AI – Artificial Intelligence Interview Questions – Edureka
The following equation is used to represent a linear regression model:
Y=𝒃𝟎+𝒃𝟏 𝒙+ⅇ
Linear Regression – Artificial Intelligence Interview Questions – Edureka
Here,
Therefore, by using the Linear Regression model, wherein Y-axis represents the sales and X-axis denotes the time period, we can easily predict the sales for the upcoming months.
E-commerce websites like Amazon make use of Machine Learning to recommend products to their customers. The basic idea of this kind of recommendation comes from collaborative filtering. Collaborative filtering is the process of comparing users with similar shopping behaviors in order to recommend products to a new user with similar shopping behavior.
Recommendation System Using AI – Artificial Intelligence Interview Questions – Edureka
To better understand this, let’s look at an example. Let’s say a user A who is a sports enthusiast bought, pizza, pasta, and a coke. Now a couple of weeks later, another user B who rides a bicycle buys pizza and pasta. He does not buy the coke, but Amazon recommends a bottle of coke to user B since his shopping behaviors and his lifestyle is quite similar to user A. This is how collaborative filtering works.
Market basket analysis explains the combinations of products that frequently co-occur in transactions.
For example, if a person buys bread, there is a 40% chance that he might also buy butter. By understanding such correlations between items, companies can grow their businesses by giving relevant offers and discount codes on such items.
Market Basket Analysis is a well-known practice that is followed by almost every huge retailer in the market. The logic behind this is Machine Learning algorithms such as Association Rule Mining and Apriori algorithm:
Association Rule Mining – Artificial Intelligence Interview Questions – Edureka
For example, the above rule suggests that, if a person buys item A then he will also buy item B. In this manner the retailer can give a discount offer which states that on purchasing Item A and B, there will be a 30% off on item C. Such rules are generated using Machine Learning. These are then applied on items in order to increase sales and grow a business.
Reinforcement Learning – Artificial Intelligence Interview Questions – Edureka
In the above figure:
This problem can be solved by using the Q-Learning algorithm, which is a reinforcement learning algorithm used to solve reward based problems.
Let’s represent the rooms on a graph, each room as a node, and each door as a link, like so:
Reinforcement Learning – Artificial Intelligence Interview Questions – Edureka
Next step is to associate a reward value to each door:
Reinforcement Learning – Artificial Intelligence Interview Questions – Edureka
Now let’s try to understand how Q-Learning can be used to solve this problem. The terminology in Q-Learning includes the terms state and action:
In the figure, a state is depicted as a node, while “action” is represented by the arrows. Suppose, the Agent traverses from room 2 to room5, then the following path is taken:
Next, we can put the state diagram and the instant reward values into a reward table or a matrix R, like so:
Reinforcement Learning – Artificial Intelligence Interview Questions – Edureka
The next step is to add another matrix Q, representing the memory of what the agent has learned through experience.
The formula to calculate the Q matrix:
Q(state, action) = R(state, action) + Gamma * Max [Q(next state, all actions)]
Here, Q(state, action) and R(state, action) represent the state and action in the Reward matrix R and the Memory matrix Q.
Note: The Gamma parameter has a range of 0 to 1 (0 <= Gamma > 1).
Finally, by following the below steps, the agent will reach room 5 by taking the most optimal path:
Reinforcement Learning – Artificial Intelligence Interview Questions – Edureka
AI can be used to implement image processing and classification techniques for extraction and classification of leaf diseases.
Image Processing Using AI – Artificial Intelligence Interview Questions – Edureka
This sounds complex, let me break it down into steps:
Image Acquisition: The sample images are collected and stored as an input database.
Image Pre-processing: Image pre-processing includes the following:
Image Segmentation: It is the process of partitioning a digital image into multiple segments so that image analysis becomes easier. Segmentation is based on image features such as color, texture. A popular Machine Learning method used for segmentation is the K-means clustering algorithm.
Feature Extraction: This is done to extract information that can be used to find the significance of a given sample. The Haar Wavelet transform can be used for texture analysis and the computations can be done by using Gray-Level Co-Occurrence Matrix.
Classification: Finally, Linear Support Vector Machine is used for classification of leaf disease. SVM is a binary classifier which uses a hyperplane called the decision boundary between two classes. This results in the formation of two classes:
Therefore, AI can be used in Computer Vision to classify and detect disease by studying and processing images. This is one of the most profound applications of AI.
So these are the most frequently asked questions in an Artificial Intelligence Interview. However, if you wish to brush up more on your knowledge, you can go through these blogs:
With this, we come to an end of this blog. I hope these Artificial Intelligence Interview Questions will help you ace your AI Interview.
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