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

Explain what are some methods of reducing dimensionality?

1 85

Tell us what's the f1 score? How would you use it?

1 88

Tell us how would you approach the “netflix prize” competition?

1 80

Tell me what are your favorite use cases of machine learning models?

1 101

Tell us what is the difference between supervised and unsupervised machine learning?

1 77

Tell us what are some differences between a linked list and an array?

1 154

Explain me how would you handle an imbalanced dataset?

1 86

Explain me what's the trade-off between bias and variance?

1 111

Can you name some feature extraction techniques used for dimensionality reduction?

1 95

Tell me how is knn different from k-means clustering?

1 120

Tell us how do bias and variance play out in machine learning?

1 101

Tell us an example where ensemble techniques might be useful?

1 98

Can you explain what is the difference between inductive machine learning and deductive machine learning?

1 159

Tell us what kind of problems does regularization solve?

1 94

Tell us what is decision tree classification?

1 206

Un-Answered Questions { Artificial Intelligence }

Explain the concept of SHAP and its role in XAI.

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

Provide examples of industries where fairness in AI is particularly critical.

Explain the role of GANs (Generative Adversarial Networks) in art creation.

How does AI intersect with human bias and societal inequities?

Do you have research experience in machine learning?

How can you detect bias in AI models?

What does "accelerating AI functions" mean, and why is it important?

What is your understanding of the different types of cloud-based machine learning services?

102

How does explainable AI (XAI) improve trust in AI systems?

105

Why is it beneficial to run AI models on edge devices (IoT)?

How can federated learning be used to train AI models?

109

How do you approach deployment of AI models?

100

What are some techniques for developing low-power AI models?

How can preprocessing techniques reduce bias in datasets?

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