Q1. A stakeholder asks: "What's the difference between AI, ML, and Deep Learning? Use an analogy with building a house."

AI is the goal of building a house (intelligent behavior).
ML is using blueprints from data (learning patterns).
Deep Learning is using specialized machinery (neural networks) to build very complex structures, e.g., multi-floor designs.

Q2. You are asked to recommend a solution for predicting stock prices. Would you choose traditional programming, ML, or DL? Why?

ML (e.g., linear regression, random forests) because stock data has patterns but not extremely high-dimensional.
DL could overfit due to limited data.
Traditional rules are impossible to hand-code.

Q3. A client wants to detect faces in images. Explain why deep learning is preferred over classical ML (e.g., SVM with HOG features).

Deep learning automatically learns hierarchical features from raw pixels; classical ML requires handcrafted features (e.g., HOG).
Deep learning scales with data and achieves higher accuracy on complex visual tasks.

Q4. You have a dataset of 100 customers to predict churn. Which approach would you use: rule-based, ML, or DL? Why?

Rule-based if patterns are simple and known.
ML (e.g., logistic regression) with small data is acceptable.
DL would overfit severely.
Rule-based may be too brittle; ML is a balance.

Q5. A chess engine uses minimax search without learning. Is this AI? Is it ML? Why?

It is AI (problem-solving by search), but not ML (no learning from data).
It uses predefined heuristics.
ML would involve learning evaluation functions from game outcomes.