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Week 2 Quiz :Convolutional Neural Networks in TensorFlow (DeepLearning.AI TensorFlow Developer Professional Certificate) Answers 2025

1. Question 1

When using image augmentation with image_dataset_from_directory, what happens to your raw image data on-disk?

  • ❌ It gets overwritten, so be sure to make a backup

  • ❌ A copy is made and the augmentation is done on the copy

  • ✅ Nothing, all augmentation is done in-memory

  • ❌ It gets deleted

Explanation: Augmentation only happens in RAM, no file on disk is modified.

2. Question 2

How does image augmentation help solve overfitting?

  • ❌ It slows down the training process

  • ✅ It manipulates the training set to generate more scenarios for features in the images

  • ❌ It manipulates the validation set

  • ❌ It automatically fits features with image processing

Explanation: Augmentation creates varied versions of the training data, helping the model generalize.

3. Question 3

Using image augmentation simulates having a larger variation of images.

  • ❌ False

  • ✅ True

Explanation: Augmentation produces transformed versions (rotations, flips, zooms), mimicking a larger and more diverse dataset.

4. Question 4

When using image augmentation, model training gets…

  • ❌ slower

  • ❌ faster

  • ❌ stays the same

  • ❌ much faster

Correct Answer:
✅ slower

Explanation: Augmentation adds extra preprocessing, so each batch takes slightly more time.

5. Question 5

Training data has people facing left; you want to classify right-facing people. What should you use?

  • ❌ Use the ‘flip’ parameter of image_dataset_from_directory

  • ❌ Use RandomFlip vertical

  • ✅ Use RandomFlip layer and set mode='horizontal'

  • ❌ Use flip parameter & set horizontal

Explanation: A horizontal flip effectively creates right-facing versions of left-facing people.

6. Question 6

How do you use image augmentation in TensorFlow?

  • ❌ Write a plugin

  • ✅ Using preprocessing layers from the Keras Layers API

  • ❌ tf.augment API

  • ❌ keras.augment API

Explanation: Modern TensorFlow uses Keras preprocessing layers like RandomFlip, RandomRotation.

7. Question 7

Why does training become slower after adding augmentation?

  • ❌ Training makes more mistakes

  • ❌ More data to train on

  • ✅ Image preprocessing takes cycles

  • ❌ Augmented data is bigger

Explanation: Augmentation operations (flip, rotate, zoom) require extra compute time per step.

8. Question 8

What does the fill_mode parameter do?

  • ❌ There is no fill_mode

  • ❌ Creates random noise

  • ✅ Recreates lost information after transformations

  • ❌ Masks background

Explanation: fill_mode decides how blank pixels created by rotation/zoom/shear should be filled.

🧾 Summary Table

Q# Correct Answer Key Concept
1 Nothing, all augmentation in-memory Augmentation does not touch disk files
2 Augments training data to create scenarios Helps prevent overfitting
3 True Augmentation simulates dataset expansion
4 Slower Extra computation needed
5 RandomFlip horizontal Creates mirrored right-facing images
6 Keras preprocessing layers TF official augmentation method
7 Preprocessing takes cycles Slower training
8 Recreates missing pixels fill_mode behavior