Top Interview Questions In Artificial Intelligence

These AI-based questions and answers are handpicked and focussed on understanding prospective candidates' in-depth and practical knowledge of implementing artificial intelligence at scale, and at an enterprise level. Organizations these days are keen to differentiate theoretical skills and practical skills which can only be gained through actual hands-on building activities. The artificial intelligence questions for the interview under consideration are targeted to analyze and assess these hands-on skills and anticipate the challenges in scaling and applying Artificial Intelligence at the enterprise level.

Que.1. What’s the most popular programming language used in AI?

Ans.1. Multiple languages are leveraged to bring an AI solution to life but python is slowly taking the pole position among top developers. The reason behind Python becoming one of the most popular languages used in Artificial Intelligence today is that it is created to be straightforward. Minimal coding efforts and faster speed are what the field demands. Python delivers exactly that and allows testing of the algorithm without even running them. Python can also be integrated into most of the other languages used in ML.

Next comes Java. Java works on the WORA (Once Written Read/Run Anywhere) principle which made it sought-after. Java has Swing and SWT incorporated, which make the graphics and interfaces look polished. Java is not only made easy to learn but it’s also easy to apply on different platforms, provides simpler debugging of codes and automatic memory manager.

Thirdly, Prolog is also quite favoured for purposes of creating graphical user interfaces (GUI) and chatbots. It is still thought of as the most dependable language for the development of AI systems as the implementation of algorithms happens through logical inferences and former statements.

Multiple certifications offer a deep dive to first build a strong foundation in one of these languages. Preferably python, before they continue on with advanced concepts. A student can leverage these courses to build a strong foundation while building AI solutions.

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Que.2. In your opinion, how will AI impact application development?

Ans.2. The adoption of Artificial Intelligence techniques have already started to be accepted by industries globally in order to ameliorate their business processes. The major impact of integrating AI is the emergence of personalized features within apps. To name a few more, a user's voice command feature can be drastically improved by using Machine Learning and NLP algorithms. Secondly, AI can analyse large amounts of data that humans can’t. This advantage is already being used by the development of programs to read each bit of information in a humane manner for obtaining an efficient natural solution faster. Lastly, as most neural networks are simplified, this eliminates the need for searching the right algorithm. It makes the work of developers effortless as they would only be required to evaluate and influence data that enters the networks.

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Que.3. What is knowledge representation in Artificial Intelligence?

Ans.3. Knowledge Representation is a tool to represent data from reality in a computer tractable form for the transformation of information into the natural language. The four widely known features associated with knowledge representation are Inferential Adequacy, Inferential Efficiency, Acquisitional efficiency and Representational accuracy. The major need of knowledge representation is to make the information transparent and easier to grasp. Additionally, the representation should reveal all-natural constraints to allow analyses of the influence one object has on another. Knowledge representation also quashes dispensable information which reduces complications. Lastly, as they are easily applied using standard computing procedures, it enables the data to be widely computable.

These concepts are covered in details in multiple certifications that a participant might enrol to gain deep insights and also understand how to integrate multiple technologies to create a practical industry-wide solution

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Que.4. What’s a random forest? Could you explain its role in AI?

Ans.4. A random forest is a machine learning algorithm that is built from decision tree algorithms to address regression and classification tasks. It utilizes the results of an enormous number of diverse decision trees to make the most probable predictions. It also offers a reduction in losing data in a black box. Its working consists of a single tree with branches. The branches are made by allocating information to a class keeping in mind its properties using rules. The branches increase exponentially until a specific level of results are acquired.

It can make an efficient prediction without hyper-parameter tuning. Importantly, every random forest tree includes a category of features that are randomly selected at the node’s splitting point. It is also known for utilizing ensemble learning, which reduces the overfitting of datasets and makes it more accurate than the decision tree algorithm which is why it is preferred.

Algorithms must be learned as a group so that one also understands the differences while making a choice among them. Multiple courses in ML and AI leverage this knowledge to target the differences and use cases among various algorithms.

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Que.5. How would you go about choosing an algorithm to solve a business problem?

Ans.5. Each business problem has its own set of novel requirements and difficulties. Therefore, the preliminary steps include identifying the business problem thoroughly. Especially the objective of the problem is either operational or strategic might reduce probable options of the right algorithm by half. Secondly, writing pseudocode hasn’t become a general practice yet. However, the process of writing pseudocode provides clarity after which just changing each line into the actual code makes the process more efficient and less error prone.

Lastly, you can employ modules like Hunga Bunga by Scikit which allows you to apply all the algorithms of your choice at once and gives an output about each algorithm’s accuracy. Auto ML is another tool using which one can determine the algorithm type. Every algorithm is unique in terms of application and the data type that they target. Simplified knowledge of these algorithms helps in understanding their application and differentiation.

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Que.6. What Is Tensorflow, and What Is It Used For?

Ans.6. Google’s Tensorflow is known so due to the tensors which act as the input in the form of a multidimensional array. It enables developers to visualise the neural network’s creation using Tensorboard. The principle of this concept is that once the tensor is provided, a set of standard operations are applied to it and as a result of differential programming a modified tensor is generated, hence the name TensorFlow.

It is used as a foundation library to build Deep Learning models firsthand. TensorFlow also caters representation of the dataflow graphs and structures to describe the flow of data through a graph. It is essentially used for pre-processing information, followed by generation of model and analysis of the same, allowing faster numerical computing as compared to other tools.

Various courses leverage TensorFlow to demonstrate their practical implementation and industry-wide application. The tool is open source which makes it easy to play around and leverage within multiple domains without the need for licensing and tackling problems of scalability.

Que.7. What Are Constraint Satisfaction Problems?

Ans.7. The constraint satisfaction problem (CSP) is a problem that presents itself along with a set of limitations. The three major elements of the same are a finite set of variables (V), constraints (C) and domains (D) which provide probable solutions which can be partial or complete. Choosing the one which assures all constraints is like finding the shortest path joining two nodes in a graph. CSP allows developers to arrange the assignments in any order they desire, unlike classical tools which require a specific sequence of actions.

Additionally, in classical search, the algorithms are improved by developing problem-specific heuristics which guide the search. On the contrary, CSPs allow a faster backtracking solver feature in domain-independent techniques. In a nutshell, if the developer satisfies the requirements of obtaining a state-space and has a notion of the solution then that is all which is needed to solve a CSP.

Que.8. What does FOPL stands for and what role does it play in Artificial Intelligence?

Ans.8. First-Order Predicate Logic (FOPL) is a manner for formal representation of Natural Language (NL) text. FOPL is the foundation of symbolic computing utilized for deductive reasoning based on facts. Unlike propositional logic, FOPL can represent individual properties of a problem and predict patterns. The syntax and semantics are the core elements of FOPL. The syntax represents the predicates using quantifiers. The symbols which are used for representation include the Predicate symbols which represent relations, the Constant symbols which represent objects and Function symbols.

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Que.9. Mention a technique that helps to avoid overfitting in a neural network.

Ans.9. Overfitting in a neural network takes place if a model is trained to learn the noise of the data instead of the patterns. This occurs if the number of training sets are few upon comparison with the complexity of the model leading to the integration of high variance. However, it is difficult to judge overfitting models as it performs well on trained data. It is only the untrained data upon which the performance becomes poor. One of the major techniques utilized to overcome overfitting is regularization.

Regularization depends on the kind of learner being implemented. The two types of regularization are Ridge Regression and Lasso Regression. The manner of designating a specific penalty to the coefficients is what sets these two apart. Regularization is applied to a model training by integrating a regularization term to the loss function which penalizes the model complexity.

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Que.10. What is an Artificial Neural Network? Name some of the commonly used ones.

Ans.10. The artificial neural network mimics the human brain in order to enable computers to develop models and make decisions in a human manner. The artificial neuron network has neurons which are called nodes. These nodes join the network under various layers namely Input layer, Hidden layer 1, Hidden layer 2, and Output Layer. An artificial neural network is quite popular because it has the ability to process numerous tasks at the same time and has the ability to work even with incomplete knowledge as long as the information lost is not very significant.

However, in today’s world of demanding high speed, the artificial neural network is reduced to a specific value of the error which makes the duration of the network unknown. This is due to the absence of a structure. The entire concept is based on trial and error. Feed Forward artificial neural network is commonly used as it efficiently recognizes input patterns and suggests how to evaluate them. The Feedback artificial neural network also used correct errors for optimization.

It’s important for one to understand how these networks are trained and improved overtime. Multiple online certifications offer capstone projects which offer an end-to-end view on how a practical industry-wide use case is solved. One can take notes from such projects and implement them over public data to further sharpen their skills.

The idea of reading through these basic interview questions on artificial intelligence is to understand how the actual implication of an AI solution takes place at scale. They also help a prospective candidate in being better prepared and answering some questions that are specifically designed to outline the clarity of concept and pragmatic information. The list is indicative and not exhaustive. However, it can be used as a quick cheat sheet to brush up major concepts and ideas on applied AI at scale.

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