Ready to crack your AI interview in 2026? Recruiters have shifted focus from basic algorithms to modern topics like Generative AI, LLMs, and AI Agents. We have curated the Top 40+ Artificial Intelligence Interview Questions, covering fundamental logic, search strategies, and modern architecture, to help you land your dream role. Let’s dive in!
Q. Difference between AI, ML, and DL?
| Aspect | Artificial Intelligence (AI) | Machine Learning (ML) | Deep Learning (DL) |
|---|
| Definition | AI is the broader technology that enables machines to simulate human intelligence and decision-making. | ML is a subset of AI that allows systems to automatically learn patterns from data and improve performance. | DL is a subset of ML that uses multi-layer neural networks to learn complex patterns from massive datasets. |
| Core Goal | Make machines think and act like humans. | Enable machines to learn from data without explicit programming. | Enable machines to learn complex representations automatically. |
| Technology Scope | Broad field including ML, robotics, NLP, computer vision, and reasoning systems. | Focused on algorithms that learn from historical data. | Focused on deep neural network architectures. |
| Data Requirement | Can work with limited or rule-based inputs. | Requires moderate volumes of training data. | Requires very large datasets for effective training. |
| Human Involvement | Often requires human-defined rules and logic. | Requires human effort for feature engineering and tuning. | Automatically extracts features with minimal manual effort. |
| Computational Requirement | Moderate computational needs. | Image recognition, voice assistants, and self-driving vehicles. | Very high computation using GPUs/TPUs. |
| Popular Applications | Chatbots, robotics, expert systems, virtual assistants. | Fraud detection, recommendations, spam filtering. | Image recognition, voice assistants, self-driving vehicles. |
| Real-world Examples | Siri, Alexa, autonomous systems. | Netflix recommendations, credit scoring. | Face recognition, voice recognition, and medical imaging AI. |
Q. What is Generative AI, and how is it different from Discriminative AI?
Generative AI is a form of artificial intelligence that has the capacity to create new content such as text, images, sounds, and code. Unlike other forms of artificial intelligence that only analyze information, generative AI has the capacity to create original information.
| Feature | Generative AI | Discriminative AI |
|---|
| Goal | Create new data instances. | Classify or predict existing data. |
| Function | Learns the distribution of data. | Learns the boundary between classes. |
| Examples | ChatGPT, Midjourney, Gemini. | Spam filters, Face recognition. |
Q. What are Large Language Models (LLMs)?
A Large Language Model (LLM) is a deep learning algorithm that has the capacity to recognize, summarize, translate, predict, generate text, and other forms of content based on knowledge gained from massive data sets.
They are based on the Transformer architecture and are trained on billions of parameters.
- Key Capabilities: Sentiment Analysis, Coding, Creative Writing, and Mathematics
- Reasoning. Examples: GPT-4 (OpenAI), Claude (Anthropic), Llama (Meta)
Q. What is an AI Agent (Agentic AI) and how does it differ from a passive LLM?
An AI Agent is an autonomous system that can perceive its environment, reason, and take actions to achieve a specific goal without constant human input.
Passive LLM (e.g., ChatGPT): Waits for a prompt -> Gives an answer -> Stops.
AI Agent (e.g., AutoGPT): Receives a goal (“Book a flight”) -> Breaks it into tasks -> Searches the web -> Compares prices -> Books the ticket -> Reports back.
Table of Contents:
The Transformer is a type of neural network architecture that was introduced by Google in 2017 in a paper titled ‘Attention Is All You Need.’ It utilizes a type of mechanism called Self-Attention to determine the level of importance of each word in a sentence, irrespective of the word’s position.
Why is it preferred over RNNs:
- Long-Range Dependency: The transformers perform well in retaining information from the start of a long paragraph, unlike RNNs that have “vanishing gradient” problems.
- Parallel Processing: Unlike RNNs, where words are processed one by one, the entire sentence is processed at once in the case of the Transformer model.
AI Fundamentals: Core Concepts and Generative AI
Q1. What is the Turing test?
The Turing test is a method to test a machine’s ability to match human-level intelligence. A machine is used to challenge human intelligence, and when it passes the test it is considered intelligent. Yet a machine could be viewed as intelligent without sufficiently knowing how to mimic a human.
Q2. List the programming languages used in AI.
Q3. What is the difference between Strong Artificial Intelligence and Weak Artificial Intelligence?
| Weak AI | Strong AI |
|---|
| Narrow application, with very limited scope | Widely applied, with vast scope |
| Good at specific tasks | Incredible human-level intelligence |
| Uses supervised and unsupervised learning to process data | Uses clustering and association to process data |
| E.g., Siri, Alexa, etc. | E.g., Advanced Robotics |
Q4. What is Prompt Engineering?
Prompt Engineering is the process of designing and refining inputs to a Generative AI model, like ChatGPT, to obtain a certain output. It requires understanding how the model processes language to prevent errors.
Common Techniques:
- Zero-shot Prompting: The prompt is used by the model without an example.
- Few-shot Prompting: Providing a few examples of the desired output format in the prompt.
- Chain of Thought: The model will be asked to describe how it has arrived at a particular answer before providing the final answer.
Q5. What is “Chain of Thought” (CoT) prompting?
Chain of Thought (CoT) is a prompting technique where the model is encouraged to explain its reasoning step-by-step before giving the final answer. This significantly improves accuracy for complex math or logic problems by reducing the chance of the model “guessing” the wrong output.
AI Logic: Search Algorithms and Problem Solving
Q6. What is the Tower of Hanoi?
Tower of Hanoi is a mathematical puzzle that shows how recursion might be utilized as a device in building up an algorithm to take care of a specific problem. Using a decision tree and a breadth-first search (BFS) algorithm in AI, we can solve the Tower of Hanoi.
Q7. What is an A* algorithm search method?
A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.
Q8. What is a breadth-first search algorithm?
A breadth-first search (BFS) algorithm, used for searching tree or graph data structures, starts from the root node, then proceeds through neighboring nodes, and further moves toward the next level of nodes.
Till the arrangement is found, it produces one tree at any given moment. As this pursuit can be executed utilizing the FIFO (first-in, first-out) data structure, this strategy gives the shortest path to the solution.
Q9. What is a depth-first search algorithm?
Depth-first search (DFS) is based on LIFO (last-in, first-out). A recursion is implemented with the LIFO stack data structure. Thus, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.
Q10. What is an iterative deepening depth-first search algorithm?
The repetitive search processes of level 1 and level 2 happen in this search. The search processes continue until the solution is found. Nodes are generated until a single goal node is created. The stack of nodes is saved.
The uniform cost search performs sorting in increasing the cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has the same cost. It investigates ways in the expanding order of cost.
Q12. Explain Alpha-Beta pruning.
Alpha–Beta pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to ‘n’ depths and can prune the entire subtrees and leaves.
An AI system uses game theory for enhancement; it requires more than one participant, which narrows the field quite a bit. The two fundamental roles are as follows:
- Participant design: Game theory is used to enhance the decision of a participant to get maximum utility.
- Mechanism design: Inverse game theory designs a game for a group of intelligent participants, e.g., auctions.
Q14. What is an expert system? What are the characteristics of an expert system?
An expert system is an Artificial Intelligence program that has expert-level knowledge about a specific area and how to utilize its information to react appropriately. These systems have the expertise to substitute a human expert. Their characteristics include:
- High performance
- Adequate response time
- Reliability
- Understandability
Q15. List the advantages of an expert system.
- Consistency
- Memory
- Diligence
- Logic
- Multiple expertise
- Ability to reason
- Fast response
- Unbiased in nature
Q16. What is fuzzy logic?
Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF-THEN rules.
Q17. What is FOPL?
First-order predicate logic(FOPL) is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.
Q18. What is partial-order planning?
A problem has to be solved in a sequential approach to attain the goal. The partial-order plan specifies all actions that need to be undertaken, but specifies an order of the actions only when required.
Q19. Differentiate between supervised, unsupervised, and reinforcement learning.
| Differentiation Based on | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
|---|
| Features | The training set has both predictors and predictions. | The training set has only predictors. | It can establish state-of-the-art results on any task. |
| Algorithms | Linear and logistic regression, support vector machine, and Naive Bayes | K-means clustering algorithm and dimensionality reduction algorithms | Q-learning, state-action-reward-state-action (SARSA), and Deep Q Network (DQN) |
| Uses | Image recognition, speech recognition, forecasting, etc. | Preprocessing data, pre-training supervised learning algorithms, etc. | Warehouses, inventory management, delivery management, power system, financial systems, etc. |
Q20. What is the difference between inductive, deductive, and abductive Machine Learning?
| Inductive Machine Learning | Deductive Machine Learning | Abductive Machine Learning |
|---|
| Learns from a set of instances to draw a conclusion | Derives the conclusion and then improves it based on the previous decisions | It is a Deep Learning technique where conclusions are derived based on various instances |
| Statistical Machine Learning, such as KNN (K-nearest neighbor) or SVM (Support Vector Machine) | Machine Learning algorithm using a decision tree | Deep neural networks |
| A ⋀ B ⊢ A → B (Induction) | A ⋀ (A → B) ⊢ B (Deduction) | B ⋀ (A → B) ⊢ A (Abduction) |
Q21. What is the Bias–Variance tradeoff?
Bias error is used to measure how much on average the predicted values vary from the actual values. In case a high-bias error occurs, we have an under-performing model.
Variance is used to measure how the predictions made on the same observation differ from each other. A high-variance model will overfit the dataset and perform badly on any observation.
Q22. What is overfitting? How is overfitting fixed?
Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.
Overfitting can be prevented by using the following methods:
- Early stopping
- Ensemble models
- Cross-validation
- Feature removal
- Regularization
Model accuracy, a subset of model performance, is based on the model performance of an algorithm. Whereas model performance is based on the datasets we feed as inputs to the algorithm.
Q24. Define F1 score.
The F1 score is the weighted average of precision and recall. It considers both false positive and false negative values into account. It is used to measure a model’s performance.
Q25. What is a recommendation system?
A recommendation system is an information filtering system that is used to predict user preference based on choice patterns followed by the user while browsing/using the system.
Q26. What is “Explainable AI” (XAI) and why is it important?
Explainable AI (XAI) refers to methods and techniques that allow human users to comprehend and trust the results and output created by machine learning algorithms. It aims to solve the “Black Box” problem, where an AI makes a decision (e.g., rejecting a loan) but the developers cannot explain why it made that decision.
Q27. What is “Hallucination” in AI, and how can it be mitigated?
Hallucination occurs when a Generative AI model confidently generates false or nonsensical information.
Mitigation: Use RAG (Retrieval-Augmented Generation) to ground answers in facts, set the model “Temperature” to 0 (more deterministic), or use human-in-the-loop verification.
Q28. How does face verification work?
Face verification is the process of confirming whether two facial images belong to the same person. It is widely used in applications such as mobile phone unlocking, social media tagging, banking authentication, and identity verification systems.
Step-by-Step Working Process
| Stage | Description |
|---|
| Input Capture | The system captures an image or video frame of a face. |
| Face Detection | An AI model detects the presence and location of a face in the image. |
| Feature Extraction | Deep learning models extract unique facial features such as eye distance, nose shape, and facial contours. |
| Face Encoding | Facial features are converted into a numerical representation (face embedding). |
| Matching & Comparison | The encoded face is compared with stored facial data. |
| Verification Result | The system confirms whether both faces belong to the same person. |
Role of Deep Learning
Modern systems use deep neural networks (CNN-based models) trained on millions of images to learn unique facial patterns and variations like lighting, angle, and expression.
Face verification systems in Python often use:
- OpenCV for face detection and image processing
- NumPy for numerical operations
- face_recognition library for face encoding and matching
- Deep learning frameworks like TensorFlow or PyTorch
Q29. List the steps involved in Machine Learning.
- Data collection
- Data preparation
- Choosing an appropriate model
- Training the dataset
- Evaluation
- Parameter tuning
- Predictions
AI Architecture: Neural Networks and Deep Learning
Q30. What is ANN?
An Artificial Neural Network (ANN) is a computational model based on the structure of the Biological Neural Network(BNN). The human brain has billions of neurons that collect, process information, and drive meaningful results out of it. The neurons use electrochemical signals to communicate and pass the information to other neurons. Similarly, an ANN consists of artificial neurons called nodes connected with other nodes, forming a complex relationship between the output and the input.
There are three layers in the Artificial Neural Network:
- Input Layer: The input layer has neurons that take the input from external sources like files, data sets, images, videos, and sensors.
- Hidden Layer: The hidden layer receives the data from the input layer and uses it to derive results and train several Machine Learning models.
- Output layer: After processing, the data is transferred to the output layer for delivering it to the outside environment.
Q31. What are the advantages of neural networks?
- Require less formal statistical training
- Have the ability to detect nonlinear relationships between variables
- Detect all possible interactions between predictor variables
- Availability of multiple training algorithms
Q32. What is a perceptron in Machine Learning?
Perceptron is an algorithm that is able to simulate the ability of the human brain to understand and discard; it is used for the supervised classification of the input into one of the several possible non-binary outputs.
Q33. What is a Backpropagation Algorithm?
Backpropagation is a core algorithm used to train neural networks by reducing prediction errors through repeated learning cycles. It works by calculating the error at the output layer and propagating it backward to update model weights for improved accuracy.
Backpropagation is widely used in image recognition, speech processing, and optical character recognition (OCR).
Q34. What is a cost function?
A cost function is a scalar function that quantifies the error factor of the neural network. The lower the cost function better the neural network. For example, while classifying the image in the MNIST dataset, the input image is digit 2, but the neural network wrongly predicts it to be 3.
Q35. List different activation neurons or functions.
- Linear neuron
- Binary threshold neuron
- Stochastic binary neuron
- Sigmoid neuron
- Tanh function
- Rectified linear unit (ReLU)
Q36. How are route weights optimized to reduce the error in the model?
Weights in AI determine how much influence the input is going to have on the output. In neural networks, algorithms use weights to process the information and train the model. The output is expected to be the same as the target attributes.
However, the output may have some errors, which need to be rectified to produce the exact output. For example, in the Backpropagation algorithm, when there is an error in the output, the algorithm will backpropagate to the hidden layer and reroute the weights to get an optimized output.
Q37. What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation, or RAG, is a technique that seeks to optimize the output of an LLM by accessing an authoritative external knowledge base outside of the training data prior to generating a final output.
Why is it used?
- Fixes Hallucinations: It provides factual documents rather than probabilities to anchor the answer from the AI.
- Up-to-date Info: While standard LLMs are only up to a certain knowledge cutoff (e.g., 2023), RAG allows for current information (e.g., today’s stock prices, policies of individual companies) in real-time.
AI Applications and Real World Impact
Q38. List some applications of AI.
Here is a list of some applications of AI:-
Q39. What are the Examples of AI in real life?
AI is widely used today to automate tasks, improve decision-making, and enhance user experiences across industries.
Common Real-Life Applications of AI
| Application | How AI is Used |
|---|
| Automated Essay Grading | AI systems evaluate assignments and essays on online learning platforms using trained neural networks. |
| Recommendation Systems | Platforms like Netflix, Amazon, and YouTube suggest content based on user behavior and preferences. |
| Navigation & Travel | Google Maps and GPS systems use AI to find optimal routes by analyzing traffic and road conditions. |
| Fraud Detection | Banks use AI models to detect suspicious transactions and prevent financial fraud in real time. |
| Autonomous Vehicles | Self-driving systems use AI, sensors, and cameras to navigate roads and reduce accidents caused by human error. |
Q40. List the applications of fuzzy logic.
- Facial pattern recognition
- Air conditioners, washing machines, and vacuum cleaners
- Antiskid braking systems and transmission systems
- Control of subway systems and unmanned helicopters
- Weather forecasting systems
- Project risk assessment
- Medical diagnosis and treatment plans
- Stock trading
Q41. List the applications of Machine Learning.
- Image, speech, and face detection
- Bioinformatics
- Market segmentation
- Manufacturing and inventory management
- Fraud detection, and so on
Salary Trends in Artificial Intelligence
You have done the legwork, you have tackled those tough AI interviews, and your hard work has paid off when you see the incredible career opportunities available to you in this new field!
2026 Market Trends: What to Expect
- Industry-Wide Adoption: Hiring is aggressive not just in Tech, but in Healthcare, Finance (BFSI), and Retail.
- Explosive Growth: AI/ML job openings in India surged by 38% in Q1 FY26.
- Hybrid Profiles: Companies now demand “Hybrid” skills—coding combined with product understanding and ethical reasoning.
- Project Over Degree: A GitHub portfolio showcasing real-world GenAI projects is now more valuable than a generic certification.
Job Opportunities in AI
As you explore the fascinating world of AI, you may be wondering, “What roles can I find in this field?” The good news is that AI is very diverse. It provides many exciting paths depending on your interests and skills.
| Job Role | Description |
|---|
| Machine Learning Engineer | Use big data tools and programming frameworks to create production-ready, scalable models that can handle real-time data. |
| Data Scientist | Use various technology tools, processes, and algorithms to extract knowledge from data and identify meaningful patterns. |
| Business Intelligence Developer | Process complex internal and external data to identify trends. For example, in a financial services company. |
| Research Scientist | They ask new and creative questions to be answered by AI. |
| Big Data Engineer/Architect | Develop ecosystems that enable various business verticals and technologies to communicate effectively. |
| Software Engineer | Develop and maintain the software that data scientists and architects use. |
| Software Architect | Design and maintain systems, tools, platforms, and technical standards for artificial intelligence technology. |
Conclusion
Artificial Intelligence is the science of solving problems with logic, search, and neural networks. Dominating these questions, especially on the topics of Generative AI and Agentic AI, will demonstrate that you are a future-ready candidate. To move from concepts to code, check out our Deep Learning Interview Questions and Machine Learning Interview Questions next. You can also enroll in our Advanced Certification in AI, Advanced Certification in Data Science and AI, or Generative AI course and get certified today. Good luck!
Frequently Asked Questions
Q1. What are the different job roles available in AI?
Apart from the traditional Data Scientists, the field now also includes AI Engineers for system architecture and Machine Learning Engineers for predictive models. New roles that are expected to be added to the field in 2026 include Prompt Engineers for optimization of LLMs, AI Ethics Officers for compliance, and Computer Vision Engineers for image processing-related work.
Q2. What is the typical interview process for an AI Engineer?
For top companies, the process generally follows this four-round pattern: screening/aptitude test, technical coding test (using Python or SQL), in-depth questions on AI concepts (Transformers, Overfitting), and system design (for example, design a chatbot). The final round is a behavioral test for culture fit.
Q3. Which top companies are hiring for AI professionals?
Demand is high in all sectors. Tech leaders such as Google, Microsoft, Meta, and NVIDIA are big recruiters. Other top recruiters include AI-focused startups such as Anthropic and Databricks. Other sectors, such as Finance (J.P. Morgan), Automotive (Tesla), and Healthcare, are also big recruiters.
Q4. What is the average salary of an AI Engineer?
AI job positions also attract a premium. In India, for instance, freshers get ₹6-12 LPA, while experienced engineers with 3-5 years of experience get ₹20-40 LPA. In the USA, entry-level positions start at $120,000, and experts can easily command more than $250,000
Q5. Do I need a PhD to get a job in AI?
No, a PhD is not required for most engineering roles, a Bachelor’s degree with an excellent portfolio and hands-on Gen AI projects is enough. However, for the specific role of a Research Scientist, inventing new algorithms, a PhD is required.