20 Interview questions Artificial Intelligence

Artificial Intelligence Fundamentals

1. What is the difference between AI, Machine Learning (ML), and Deep Learning (DL)?

• AI is the broader concept of creating machines that can perform tasks requiring human intelligence.
• ML is a subset of AI where models learn patterns from data without explicit programming.
• DL is a subset of ML that uses neural networks with multiple layers (deep neural networks) for feature extraction and pattern recognition.

2. What are the different types of AI?

1. Narrow AI (Weak AI): AI designed for a specific task (e.g., Siri, Chatbots).
2. General AI (Strong AI): AI with human-like cognitive abilities (not yet achieved).
3. Super AI: AI surpassing human intelligence (theoretical stage).

3. What are some real-world applications of AI?

• Healthcare: Medical diagnosis (AI-assisted radiology).
• Finance: Fraud detection, risk assessment.
• Retail: Recommendation systems (Amazon, Netflix).
• Autonomous Vehicles: Tesla self-driving cars.
• NLP: Chatbots, sentiment analysis.

4. What is Reinforcement Learning (RL)? Give an example.

RL is a learning paradigm where an agent interacts with an environment to maximize cumulative rewards.
Example: AlphaGo (DeepMind) learns to play Go by receiving rewards for winning moves.

Neural Networks & Deep Learning

5. What is the difference between CNN, RNN, and Transformer models?

• CNN (Convolutional Neural Networks): Best for image processing, captures spatial features (e.g., ResNet, VGG).
• RNN (Recurrent Neural Networks): Used for sequential data, has memory but suffers from vanishing gradients (e.g., LSTMs).
• Transformer Models: Use self-attention mechanisms for NLP tasks (e.g., BERT, GPT).

6. What is the role of activation functions in neural networks?

Activation functions introduce non-linearity, allowing networks to learn complex patterns.
Common types:

• ReLU: Prevents vanishing gradients, widely used in CNNs.
• Sigmoid: Good for binary classification but suffers from saturation.
• Softmax: Used in multi-class classification.

7. How does backpropagation work in deep learning?

Backpropagation calculates gradients using the chain rule, propagates errors backward, and updates weights using gradient descent to minimize loss.

8. What are autoencoders? Where are they used?

Autoencoders are unsupervised neural networks used for:

• Dimensionality reduction (similar to PCA).
• Anomaly detection (fraud detection).
• Denoising images (removing noise from images).

9. Explain Generative Adversarial Networks (GANs).

GANs consist of:

• Generator: Creates fake data samples.
• Discriminator: Distinguishes real from fake samples.
  They compete to improve generation quality (used in deepfakes, art generation).

10. What is Transfer Learning, and when is it useful?

Transfer learning involves using pre-trained models (like BERT, ResNet) and fine-tuning them on a new dataset. It’s useful when:

• There is limited labeled data.
• The new task is similar to the original training task.

Natural Language Processing (NLP)

11. What is word embedding? Name some popular word embeddings.

Word embeddings convert words into dense vector representations to capture meaning.
Popular embeddings:

• Word2Vec
• GloVe
• FastText
• BERT embeddings

12. What is attention mechanism, and why is it important in NLP?

Attention allows models to focus on relevant parts of input sequences.
Example: In translation, attention helps focus on specific words instead of treating all words equally.
Used in Transformer models like BERT and GPT.

13. What is Named Entity Recognition (NER)? Give an example.

NER identifies proper nouns in text.
Example:
Input: “Elon Musk founded Tesla in 2003.”
Output: {Person: “Elon Musk”, Organization: “Tesla”, Date: “2003”}

14. How does BERT improve NLP tasks?

BERT (Bidirectional Encoder Representations from Transformers):

• Reads text bidirectionally, unlike previous models.
• Uses self-attention to capture relationships between words.
• Achieves state-of-the-art results in tasks like Q&A, summarization.

AI Model Optimization & Deployment

15. What are the key steps in deploying an AI model?

1. Model Training & Evaluation
2. Convert model to a deployable format (ONNX, TensorFlow Serving).
3. Use APIs (FastAPI, Flask) to serve the model.
4. Deploy on cloud platforms (AWS, GCP, Azure).
5. Monitor performance using MLflow, Prometheus.

16. What are common AI model performance issues, and how do you fix them?

• Overfitting: Use regularization (Dropout, L2), increase data.
• Underfitting: Use more complex models, reduce bias.
• Data imbalance: Use techniques like oversampling, SMOTE.

17. What is model drift, and how do you detect it?

Model drift occurs when data distribution changes over time, reducing model accuracy.
Detection:

• Compare current data vs. training data statistics.
• Use concept drift detection algorithms.
  Solution:
• Retrain model periodically with updated data.

Big Data & AI Scalability

18. How do you handle large-scale AI models that don’t fit in memory?

• Use distributed computing (Spark, Dask).
• Use data streaming (Kafka, TensorFlow Data Service).
• Optimize memory with quantization & pruning.

19. What are Feature Stores in AI?

Feature Stores centralize feature engineering & storage for reuse across models.
Benefits:

• Improves consistency (no redundant computation).
• Optimized for real-time and batch processing.
  Examples: Feast, Tecton, Vertex AI Feature Store.

20. What are AI ethics concerns? How can we mitigate AI bias?

AI ethics concerns:

• Bias in AI models (racial, gender, social bias).
• Data privacy issues.
• Explainability & accountability.

Solutions:

• Fair data collection (diverse datasets).
• Bias detection techniques (SHAP, LIME).
• Regulatory compliance (GDPR, AI audits).

Final Thoughts

These questions cover AI fundamentals, deep learning, NLP, deployment, and scalability. Do you need role-specific questions for industries like healthcare, finance, or autonomous systems? 🚀

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