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1. What does “deep” describe in a deep-learning model?
   • A neural-network architecture with multiple layers between input and output
   • A model that understands topics at a profound human level
   • A system trained only on unusually large datasets
   • A method that guarantees higher task accuracy
2. A network has 2 inputs, hidden layers of 3 and 2 units, and 1 output. With a bias for each hidden/output unit, how many weights and biases are there?
   • 14
   • 20
   • 18
   • 23
3. Why use nonlinear activations between hidden layers?
   • They convert every hidden unit into a labeled training example
   • They guarantee the network will generalize to unseen data
   • Without them, stacking linear transformations still produces a linear transformation
   • They remove the need for learned weights
4. During inference, what normally happens to the trained model’s parameters?
   • They are retrained after every input
   • They are replaced by the labels in the prompt
   • They are reset before each output
   • The model uses them to process an input without necessarily updating them
5. A model fits its training examples closely but performs poorly on comparable held-out data. What pattern does this show?
   • اوور فٹنگ
   • انڈر فٹنگ
   • Good generalization
   • A guarantee that the labels are correct
6. Why compare a deeper network with a simpler baseline on held-out examples?
   • Because simpler models always outperform neural networks
   • To see whether added complexity improves the task enough to justify its practical costs
   • Because deeper models cannot be evaluated on new data
   • To avoid checking error patterns
7. A classifier returns a score of 0.9. What does that alone establish?
   • The prediction is correct 90% of the time on every task
   • The model has been calibrated on deployment data
   • It is a model score, not proof of a calibrated 90% chance of correctness
   • The system understands the example with 90% certainty
8. What does adding more layers alone prove about a model’s understanding or usefulness?
   • That it understands inputs like a person
   • That it will outperform a simpler model
   • That its errors are harmless
   • Nothing by itself; evaluate it on the intended task and relevant examples