An Ideal Approach to Answer Artificial Intelligence Interview Questions

 

According to the US Bureau of Labor Statistics, from 2019 to 2029, there will be a 15% growth in computer and information research occupations, including jobs in AI. As of May 2020, the typical annual compensation for this position was $126,830 (around ₹1,04,49,206 crores), with the opportunity for higher pay in management positions. AI professionals are in high demand, which rewards them with excellent pay rates.

 

The following list of typical artificial intelligence interview questions and answers will help you prepare better for the roles. 

 

Commonly Encountered Artificial Intelligence Questions for Interview 

 

 

Here is a list of some of the most commonly asked questions on artificial intelligence to the candidates - 

 

 

• What benefits may artificial intelligence provide?

 

Ideal approach: Consider every benefit that artificial intelligence has over other technologies that come to mind. Then you can list each one separately with its definition. Make sure you don't repeat anything while you're responding.

 

Example: Artificial intelligence technologies improve accuracy and precision while lowering the possibility of human error. Every choice made by artificial intelligence can be based on data that has already been obtained or a set of algorithms. 

If artificial intelligence is programmed correctly, we can guarantee that there will be no errors. It enables us to have digital support available around the clock, which many businesses employ nowadays to communicate with their clients. As a result, fewer people are required to perform the same task.

 

 

• What are some of the artificial intelligence's tasks used?

 

Ideal approach: Some of the applications you use artificial intelligence may interest potential employers. You can respond to this question to demonstrate your creativity and problem-solving abilities. 

 

Example: I've worked with artificial intelligence voice-based command systems before. It emphasises language processing and speech recognition. I focused on exploring and testing various techniques for teaching the system to interpret diverse voice tones, speech patterns, and languages throughout several projects.

 

 

• In which project did you previously use weak AI?

 

Ideal approach: The interviewer might wish to speak with you in-depth to assess your skills. They want to know how you perceive this facet of artificial intelligence and how you might approach it. Use examples from your previous efforts to further illustrate your response.

 

Example: Weak artificial intelligence is constrained to a particular or limited domain. I'll provide you with an application that uses weak AI as an example. 

My former project had an email spam filter that employed an algorithm to distinguish between legitimate emails and junk mail and moved the latter to the spam folder. 

As big data is converted into information by weak AI, this information can then be used later to identify patterns and make predictions.

 

 

• What are artificial intelligence programming languages?

 

Ideal approach: You may face this fundamental query whenever you undergo a recruitment process, as it is one of the most common artificial intelligence and machine learning interview questions. The interview host wants to know more about your technological know-how and abilities. You can enumerate each one for the interviewer.

 

Example: The project's scope and requirements may influence the programming language selection. The project gets new features introduced by each programming language. Python, Java, R, LISP, and C++ are a few of the popular programming languages used in artificial intelligence applications.

 

 

• How do you select an algorithm to address problems in machine learning and artificial intelligence?

 

Ideal approach: You may have the chance to demonstrate your attention to detail, technical knowledge, and problem-solving abilities by responding to this question. 

This query reveals to the interviewer how you tackle the problems. The interviewer is also interested in learning what steps you intend to take to address them.

 

Example: I attempt to fully identify the issue before selecting an algorithm to address artificial intelligence and machine learning problems. This makes it easier for me to comprehend the situation. I may then choose the algorithm and input that will work right.

Additionally, it is vital to select a precise, scalable, and simple algorithm to resolve the problem entirely. Since meeting the deadline is essential, I concentrate on data analysis before choosing the appropriate method based on training and build times.

 

Artificial Intelligence and Data Science Interview Questions for Experienced Candidates

In this section, we will continue with the interview questions list. However, these are for experienced candidates.  

 

 

• Explain the collaborative filtering process.

 

Answer: Using a process called filtering, recommender systems may extract patterns and information from a variety of data sources, multiple agents, and cooperating viewpoints. To put it another way, the collaborative method automatically predicts human preferences or interests.

 

 

• Why is the central limit theorem important and what is it?

 

Answer: According to the central limit theorem, a statistical theory, the mean of the sample population is roughly equal to the mean of the entire population given a large enough sample size from the accessible population and a finite degree of variation.

 

 

• What does linear regression mean?

 

Answer: Predictive analysis frequently uses linear regression. It aids in our examination of two things. For instance, linear regression is utilized when comparing two characteristics of a specific entity.

Let's imagine that a house's price is influenced by two separate elements: location and size.

We must use linear regression to discover the connection between these elements. Finding the benefits or drawbacks of these two relationships is made easier with the use of linear regression.

 

 

• What does "selection bias" mean?

 

Answer: The form of inaccuracy tagged as "selection bias" occurs when the researcher chooses the participants for the study. It happens when participant selection is not done at random. 

A selection effect is another name for selection bias. It operates more efficiently, yet occasionally the study's findings could be flawed if the selection bias is not considered.

 

 

• Explain the concept of root cause analysis.

 

Answer: The root cause analysis was first used to analyse industrial accidents, but it has since been expanded into a wide range of fields. It is a method employed to pinpoint the underlying cause of a specific issue in the AI world.

 

Conclusion 

Gaining skills along with confidence are essential to your dream job. Equipping yourself with the right skill sets alone will not help you ace an interview. Skill-Lync provides interview training for students enrolled in our PG program.

While pursuing a PG program you will be ready to answer  interview questions about artificial intelligence that have been hand-picked by top data scientists and subject matter experts. In case you wish to book a demo class on any AI-related course with us, feel free to do that today!