15 Conceptual Interview Questions and Answers

Conceptual questions are those that probe your understanding of the underlying principles of a subject. They are often used in interviews for engineering and scientific positions, as well as in academic settings. While they may seem difficult at first, with a little practice, you can learn how to answer them confidently.

Whether you’re interviewing for a job or taking an exam, being able to answer conceptual questions is a valuable skill. In this guide, we’ll show you how to approach these types of questions, with examples of both common and difficult questions. With our help, you’ll be ready to tackle any conceptual question that comes your way.

1. What is a decision tree?

This question tests your knowledge of a specific concept. It also allows you to show the interviewer that you can apply what you know about this concept to a real-world situation.

Example: “A decision tree is a diagram that shows all possible outcomes for a given problem or scenario. Each outcome has its own set of conditions, and each condition leads to another branch with more conditions. The final outcome is determined by which conditions are met first. For example, if I wanted to find out how much money I would make at my current job in five years, I could use a decision tree to determine whether I stay at my current job, get a promotion or change jobs.”

2. Can you explain what an ontology is in the context of data science and AI?

This question is a great way to test your knowledge of the fundamentals of data science and AI. It also allows you to show that you can apply this foundational knowledge to real-world situations.

Example: “An ontology is a formal specification of concepts, objects, properties and relationships within a domain. In my previous role as an IT analyst, I used ontologies to create taxonomies for large sets of unstructured data. This allowed me to organize the data into categories so it could be easily analyzed by machine learning algorithms.”

3. What is the difference between descriptive, predictive, prescriptive analytics?

This question tests your knowledge of the different types of analytics and how they can be used to solve business problems. Describe each type in detail, explaining what it is and when you would use it.

Example: “Descriptive analytics are reports that describe past performance. They’re useful for understanding customer behavior and identifying trends. Predictive analytics predict future outcomes based on historical data. This information helps businesses make better decisions about their operations. Prescriptive analytics combine predictive and descriptive analytics to help companies find solutions to specific problems. For example, if a company wants to increase sales by 10%, I could use prescriptive analytics to determine which marketing strategies have been most effective in the past.”

4. What are some examples of big data problems that can be solved using machine learning?

This question is a great way to show your interviewer that you have the conceptual skills needed for this role. You can answer this question by giving examples of how machine learning and big data work together.

Example: “Machine learning is used in big data problems where there’s a lot of information, such as when companies want to analyze customer behavior or predict future trends. For example, I worked with a company that wanted to use machine learning to predict which customers were likely to leave them. The company had a large database of customer information, so we used machine learning to create algorithms that could predict whether a customer would leave based on their previous purchases.”

5. What do you understand by supervised and unsupervised learning? Give me some examples of each type.

This question is a great way to test your knowledge of the different types of learning. You can use examples from your previous experience to explain what each type of learning entails and how it works.

Example: “Supervised learning involves using labeled data to train an algorithm, which then learns to make predictions based on this information. Unsupervised learning uses unlabeled data to find patterns in the data that are useful for making predictions. This process is called clustering.”

6. What is NLP? How does it differ from NLU?

NLP is an abbreviation for natural language processing, which is a subfield of computer science that focuses on the ability to process human languages. It differs from natural language understanding (NLU), which is the ability to understand and interpret human languages. Your answer should show the interviewer you have a basic knowledge of NLP and NLU.

Example: “NLP is the ability to process human languages using computers. In contrast, NLU is the ability to understand and interpret human languages. For example, if I ask my smartphone where the nearest gas station is, it will use NLP to translate my question into machine-readable code so it can search its database for the information. If I ask the same question again later in the day, it will use NLU to remember what I am asking for.”

7. Why are algorithms used for pattern recognition important in data science?

This question is a great way to test your knowledge of the fundamentals of data science. It also allows you to show how you apply that knowledge in real-world situations. Your answer should include an explanation of what algorithms are and why they’re important for pattern recognition.

Example: “Algorithms are sets of instructions used to solve problems or complete tasks. In data science, algorithms are used for pattern recognition. They allow computers to analyze large amounts of data quickly and efficiently. This helps me determine which variables I need to consider when analyzing data. For example, if I’m looking at customer purchasing patterns, I can use algorithms to sort through all the information and find any trends.”

8. Explain the differences between batch, incremental, and real-time processing in data science.

This question tests your knowledge of the different ways to process data. It also shows that you can apply this knowledge in a real-world situation. Your answer should show that you understand how each method works and when it’s best to use them.

Example: “Batch processing is used for large amounts of data, but it takes longer than incremental or real-time processing. With batch processing, all the data is processed at once. Incremental processing is similar to batch processing, except only new data is processed. Real-time processing is done as soon as the data is available. This is useful for applications like fraud detection.”

9. What is anomaly detection?

This question tests your knowledge of a specific concept. Use your answer to show the interviewer that you can apply what you know about anomaly detection to real-world situations.

Example: “Anomaly detection is a process used by cybersecurity professionals to identify unusual activity on a network. It’s important for security teams to be able to recognize when something out of the ordinary occurs because it could indicate an intrusion or other cyberattack. In my last role, I was responsible for monitoring our company’s servers and identifying any anomalies. If I noticed anything suspicious, I would report it to my supervisor so they could investigate.”

10. What’s the difference between deep learning and machine learning?

This question is a great way to test your knowledge of two important concepts in the field. Your answer should show that you understand how they’re different and can apply them correctly.

Example: “Machine learning is a subset of artificial intelligence, which uses algorithms to make predictions based on data. Deep learning is a type of machine learning that focuses on neural networks. Neural networks are modeled after the human brain and use layers of information processing units to learn from large amounts of data. Machine learning uses deep learning to create models that allow computers to perform tasks without being programmed.”

11. Can you explain what a neural network is?

This question is a great way to test your conceptual skills. Neural networks are complex, and the interviewer wants to see if you can break down this concept into simpler terms that anyone could understand.

Example: “A neural network is a system of interconnected nodes that work together to solve problems or learn from data. It’s made up of artificial neurons that process information by receiving input, changing it based on its programming and then sending it to other neurons. The more data the network processes, the better it gets at recognizing patterns and making predictions.”

12. What are the advantages of using a virtual workspace over a physical one?

This question is a great way to assess your conceptual skills and how you apply them in the workplace. When answering this question, it can be helpful to discuss what you’ve learned about virtual workspaces from previous experience or training.

Example: “There are many advantages of using a virtual workspace over a physical one. For example, with a virtual workspace, employees have access to all their files and documents at any time. This means they don’t need to spend time looking for information that’s already available to them. Another advantage is that there’s no need for office space, which saves money on rent and other expenses. Finally, because there’s no need for an office space, employees can work remotely.”

13. What is the best way to find out if your model is underfitting or overfitting?

This question is a continuation of the previous one, and it tests your knowledge about how to use conceptual skills in modeling. Your answer should show that you know what underfitting and overfitting are and how to identify them. You can also mention some strategies for avoiding these issues when working on models.

Example: “The best way to find out if your model is underfitting or overfitting is by using validation techniques. There are two types of validation techniques—training error and testing error. Training error measures the difference between the expected output and actual output during training. Testing error measures the difference between the expected output and actual output after training. If there’s no difference between the expected and actual outputs during both training and testing, then the model isn’t underfitting or overfitting.”

14. What is ensemble modeling?

This question tests your knowledge of a specific modeling technique. Ensemble modeling is a process that uses multiple models to solve complex problems. Your answer should show the interviewer you understand how this method works and can apply it in your own work.

Example: “Ensemble modeling is a data mining technique where multiple predictive models are used together to make more accurate predictions than any single model could achieve alone. It’s often used for making predictions about future events, such as stock prices or election results. The different models use different algorithms to analyze the same problem from different perspectives. When all the models have produced their results, they’re combined into one final prediction.”

15. Can you explain what bias and variance are in the context of machine learning models?

This question is a test of your conceptual knowledge and ability to apply it in the workplace. When answering this question, define bias and variance clearly and then explain how they affect machine learning models.

Example: “Bias and variance are two important concepts in machine learning that can impact the performance of a model. Bias refers to when a model makes incorrect predictions due to errors in its algorithms or data sets. Variance occurs when a model’s predictions vary widely from actual results. In my last role, I used these concepts to troubleshoot issues with our company’s predictive modeling software.”