Artificial Intelligence Interview Questions, Answers for Freshers and Experienced asked in various Company Job Interviews

AI Algorithms (74)
AI Natural Language Processing (96)
AI Knowledge Representation Reasoning (12)
AI Robotics (183)

Artificial Intelligence Interview Questions

Questions Answers Views Company eMail

What is the difference between inductive and deductive learning?

1 170

How is KNN different from K-means clustering?

1 140

What is ROC curve and what does it represent?

1 132

What’s the difference between Type I and Type II error?

1 176

Is it better to have too many false positives or too many false negatives? Explain.

1 92

Which is more important to you – model accuracy or model performance?

1 103

What is the difference between Gini Impurity and Entropy in a Decision Tree?

1 254

What is the difference between Entropy and Information Gain?

1 151

What is Overfitting? And how do you ensure you’re not overfitting with a model?

1 128

Explain Ensemble learning technique in Machine Learning?

1 140

What is bagging and boosting in Machine Learning?

1 203

What is bagging in Machine Learning?

1 127

What is boosting in Machine Learning?

1 116

How would you screen for outliers and what should you do if you find one?

1 136

What are collinearity and multicollinearity?

1 149

Un-Answered Questions { Artificial Intelligence }

How can you optimize AI models for edge deployment?

130

How do you ensure that your models are fair and unbiased?

109

What are the benefits and risks of using AI in financial risk analysis?

How does AI intersect with human bias and societal inequities?

103

How do you ensure compatibility between Generative AI models and other AI systems?

Explain the difference between supervised, unsupervised, and reinforcement learning.

What frameworks can you use for ethical AI development?

101

How does the bias in training data affect the performance of AI models?

120

Tell us do you have research experience in machine learning?

162

What is in-processing bias mitigation, and how does it work?

How can preprocessing techniques reduce bias in datasets?

Can AI systems ever be completely free of bias? Why or why not?

What tools or practices can help secure AI models against attacks?

Explain how AI models predict stock market trends.

Explain demographic parity and its importance in AI fairness.

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