CS 7643 Verified Multiple Choice and
Conceptual Actual Frequently Tested Exam
Questions With Reviewed 100% Correct
Detailed Answers
Guaranteed Pass!!Current Update!!
Q1. What is modeling error in neural networks?
A. Error due to overfitting the training data.
B. Error from failing to find the best hypothesis during optimization.
C. Error due to the chosen model architecture not being able to represent the true
function.
D. Error from noise in the dataset.
Answer: C
Q2. As model complexity increases, how do modeling error and optimization error
typically change?
A. Both decrease.
B. Both increase.
C. Modeling error decreases, optimization error increases.
D. Modeling error increases, estimation error decreases.
Answer: C
Q3. What is estimation error?
A. When a model fits training data but fails to generalize to test data.
B. When optimization fails to converge to a minimum.
C. When noise in the dataset prevents accurate training.
, D. When model capacity is too low.
Answer: A
Q4. What is optimization error?
A. Error due to randomness in SGD updates.
B. Error caused by the algorithm failing to find weights that minimize training
error.
C. Error due to poor model generalization.
D. Error caused by adversarial examples.
Answer: B
Q5. Transfer learning is best described as:
A. Training from scratch on a new dataset.
B. Reusing features learned on a large dataset for a new task.
C. Sharing model weights between different models simultaneously.
D. Using unsupervised pretraining before supervised fine-tuning.
Answer: B
Q6. Which is a typical procedure for transfer learning with CNNs?
A. Freeze early layers, retrain fully connected layers on new data.
B. Replace the convolutional layers and retrain all weights.
C. Train only with unsupervised embeddings.
D. Remove dropout layers and retrain from scratch.
Answer: A
Q7. When does transfer learning not perform well?
A. When the target dataset is very similar to the source dataset.
B. When the target dataset is very small.
C. When the target dataset is very different from the source dataset.
Conceptual Actual Frequently Tested Exam
Questions With Reviewed 100% Correct
Detailed Answers
Guaranteed Pass!!Current Update!!
Q1. What is modeling error in neural networks?
A. Error due to overfitting the training data.
B. Error from failing to find the best hypothesis during optimization.
C. Error due to the chosen model architecture not being able to represent the true
function.
D. Error from noise in the dataset.
Answer: C
Q2. As model complexity increases, how do modeling error and optimization error
typically change?
A. Both decrease.
B. Both increase.
C. Modeling error decreases, optimization error increases.
D. Modeling error increases, estimation error decreases.
Answer: C
Q3. What is estimation error?
A. When a model fits training data but fails to generalize to test data.
B. When optimization fails to converge to a minimum.
C. When noise in the dataset prevents accurate training.
, D. When model capacity is too low.
Answer: A
Q4. What is optimization error?
A. Error due to randomness in SGD updates.
B. Error caused by the algorithm failing to find weights that minimize training
error.
C. Error due to poor model generalization.
D. Error caused by adversarial examples.
Answer: B
Q5. Transfer learning is best described as:
A. Training from scratch on a new dataset.
B. Reusing features learned on a large dataset for a new task.
C. Sharing model weights between different models simultaneously.
D. Using unsupervised pretraining before supervised fine-tuning.
Answer: B
Q6. Which is a typical procedure for transfer learning with CNNs?
A. Freeze early layers, retrain fully connected layers on new data.
B. Replace the convolutional layers and retrain all weights.
C. Train only with unsupervised embeddings.
D. Remove dropout layers and retrain from scratch.
Answer: A
Q7. When does transfer learning not perform well?
A. When the target dataset is very similar to the source dataset.
B. When the target dataset is very small.
C. When the target dataset is very different from the source dataset.
