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AI & Machine Learning Interview Questions & Answers

Deep-dive questions on neural networks, LLMs, Transformer architectures, loss functions, and MLOps deployment.

Advanced 1857 views

Explain Retrieval-Augmented Generation (RAG) Architecture vs Model Fine-Tuning

When building enterprise LLM applications, choosing between RAG (Retrieval-Augmented Generation) and Model Fine-Tuning (PEFT/LoRA) depends on data dynamics, hallucination tolerance, and context freshn...

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Advanced 1426 views

Explain the Transformer Architecture and Multi-Head Self-Attention in LLMs

The Transformer architecture , introduced in the seminal paper "Attention Is All You Need" (Vaswani et al., 2017), replaced recurrent and convolutional layers with Self-Attention mechanisms , enabling...

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Intermediate 1184 views

How do you detect and mitigate Overfitting vs Underfitting in deep neural networks?

Overfitting occurs when a model lea s the training data and noise too closely, leading to poor generalization on unseen validation/test data (high variance). Underfitting occurs when a model is too si...

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Advanced 153 views

Explain the Self-Attention mechanism in Transformer architectures and how its computational complexity scales with sequence length.

Transformer Self-Attention Mechanism Self-attention allows an input token to dynamically attend to every other token in the sequence, computing context-aware contextual representations. Mathematical F...

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Advanced 2 views

What is the Transformer architecture in Machine Learning and why did Self-Attention replace Recurrent Neural Networks (RNNs)?

Introduced in the seminal 2017 paper 'Attention Is All You Need' , the Transformer architecture revolutionized Natural Language Processing and modern Large Language Models (LLMs). Key advantages over ...

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Medium 2 views

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Easy 2 views

What is overfitting, and how can you recognize it?

Overfitting occurs when a model captures training-specific noise or patterns that do not generalize. A common sign is strong training performance with substantially weaker validation performance on re...

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Medium 2 views

A fraud model reports 99% accuracy when only 1% of transactions are fraudulent. How would you evaluate it?

A classifier that always predicts legitimate already achieves 99% accuracy, so inspect the confusion matrix, fraud-class precision and recall, and precision-recall behavior across thresholds. Estimate...

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Easy 1 views

How do supervised, unsupervised, and reinforcement learning differ?

Supervised learning learns from examples paired with target labels, such as emails labeled spam or legitimate. Unsupervised learning searches for structure without those target labels, such as custome...

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Medium 1 views

What is data leakage, and how would you prevent it in a preprocessing pipeline?

Leakage occurs when training or evaluation uses information that would not be available at prediction time or information from the held-out data. Examples include scaling before splitting, selecting f...

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Medium 1 views

When should a company choose retrieval-augmented generation instead of fine-tuning?

Use retrieval-augmented generation when answers need access to changing, private, or attributable knowledge. Retrieve authorized documents, provide relevant passages, and require grounded answers with...

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Medium 1 views

How would you investigate a deployed model whose performance has deteriorated?

Verify the monitoring and label pipeline before assuming the model changed. Compare current and training feature distributions, missing values, schema, segment mix, and prediction scores. Distinguish ...

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