AIF-C01 Fundamentals of AI and ML Practice Question
A company is training a deep learning model for image classification. Which THREE practices help reduce overfitting? (Choose three.)
⚠ Common exam trap
The AIF-C01 exam often tests the misconception that increasing model complexity (depth) or tuning the learning rate can mitigate overfitting, when in fact these changes either exacerbate the problem or address unrelated training dynamics.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
L2 regularization
L2 regularization (option A) is correct because it adds a penalty proportional to the squared magnitude of the weights to the loss function, discouraging large weights and thus reducing the model's ability to memorize training noise. Dropout (option D) is correct because it randomly deactivates a fraction of neurons during each training step, forcing the network to learn redundant, more robust feature representations instead of relying on specific units. Data augmentation (option E) is correct because it synthetically expands the training set by applying label-preserving transformations (e.g., flips, rotations, crops) to images, exposing the model to more varied inputs and improving generalization. Increasing model depth (option B) is not correct here because adding layers increases capacity, which typically makes overfitting worse rather than better. Increasing the learning rate (option C) is not correct because it only affects optimization speed and stability, and an excessively high rate can cause divergence or poor convergence, not reduced overfitting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
L2 regularization
Why this is correct
L2 regularization penalises large weights by adding a squared-magnitude term to the loss function, constraining the model's capacity to memorise training noise. This directly satisfies the stem's requirement to reduce overfitting in deep image classification, since simpler weight distributions generalise better to unseen images.
- ✗
Increasing model depth
Why it's wrong here
Increasing model depth adds parameters and capacity, which lets the network memorise training data and worsens overfitting. It is tempting because deeper networks can raise training accuracy, and depth would be the right lever when a model is underfitting and needs greater representational power.
- ✗
Increasing learning rate
Why it's wrong here
Raising the learning rate makes larger weight updates, causing unstable convergence and poorer generalisation rather than reducing overfitting. It is tempting because a higher rate shortens training time, and it would be the right adjustment when training is prohibitively slow or the model is stuck in a shallow local minimum.
- ✓
Dropout
Why this is correct
Dropout randomly deactivates a proportion of neurons during each training pass, forcing the network to learn redundant, distributed representations rather than memorising training samples. This regularisation directly counteracts the overfitting the stem describes, improving generalisation to unseen images.
- ✓
Data augmentation
Why this is correct
Data augmentation generates transformed copies of training images—rotations, flips, crops—expanding the effective dataset and exposing the model to greater visual variance. This reduces overfitting by preventing memorisation of the limited original samples, satisfying the stem's requirement to improve generalisation.
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