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 your training in machine learning and what types of hands-on experience do you have?

1 160

Explain what is the difference between inductive machine learning and deductive machine learning?

1 91

Which do you think is more important: model accuracy or model performance?

1 73

What cross-validation technique would you use on a time series dataset?

1 120

What do you think of our current data process?

1 79

When should you use classification over regression?

1 73

What is the “kernel trick” and how is it useful?

1 130

Explain machine learning in to a layperson?

1 101

What's the f1 score? How would you use it?

1 76

What are your favorite use cases of machine learning models?

1 159

An example where ensemble techniques might be useful?

1 122

What is the difference between a generative and discriminative model?

1 126

How a roc curve works?

1 80

What is bayes' theorem? How is it useful in a machine learning context?

1 90

How would you handle an imbalanced dataset?

1 229

Un-Answered Questions { Artificial Intelligence }

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

102

What are the biggest challenges you see in AI implementation across industries?

101

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

What are standardization and normalisation? Give one advantage of each over the other?

Tell us do you have research experience in machine learning?

155

Explain the difference between data bias and algorithmic bias.

How do domain-specific requirements affect AI system design?

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

105

How do Generative AI models create synthetic data?

What are some open problems you find interesting?

What are the apis outside tensorflow project?

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

Explain how AI models create realistic game physics.

How can AI be used to predict patient outcomes?

How do you identify and mitigate bias in Generative AI models?

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