This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is training a neural network for image classification. The training loss is not decreasing significantly, and the validation loss is high. Which TWO actions should the scientist take to address potential vanishing gradients?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use ReLU activation functions in hidden layers
ReLU activation functions help mitigate vanishing gradients because they output a constant gradient of 1 for positive inputs, preventing the gradient from shrinking as it propagates backward through many layers. This avoids the exponential decay of gradients that occurs with saturating activations like sigmoid or tanh, enabling effective training of deep networks.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the learning rate
Why it's wrong here
High learning rate can cause divergence but does not address vanishing gradients.
✓
Use ReLU activation functions in hidden layers
Why this is correct
ReLU does not saturate for positive inputs, reducing vanishing gradient risk.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch activation functions from ReLU to sigmoid
Why it's wrong here
Sigmoid can cause vanishing gradients due to saturation.
✓
Add batch normalization layers
Why this is correct
Batch normalization normalizes activations, preventing saturation and mitigating vanishing gradients.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove dropout layers
Why it's wrong here
Dropout is a regularization technique; removing it does not help with vanishing gradients.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse vanishing gradients with overfitting or learning rate issues, leading them to choose options like increasing the learning rate or removing dropout, which do not address the fundamental gradient propagation problem.
Detailed technical explanation
How to think about this question
Vanishing gradients occur when gradients are multiplied by small derivatives (<1) from saturating activations across many layers, causing them to approach zero. ReLU's derivative is 1 for positive inputs, so gradients flow unchanged through active neurons, but it can cause dead neurons (zero gradient for negative inputs). Batch normalization (option D) mitigates vanishing gradients by normalizing layer inputs to have zero mean and unit variance, keeping activations in the linear regime of activation functions and preventing saturation, which also stabilizes gradient flow.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use ReLU activation functions in hidden layers — ReLU activation functions help mitigate vanishing gradients because they output a constant gradient of 1 for positive inputs, preventing the gradient from shrinking as it propagates backward through many layers. This avoids the exponential decay of gradients that occurs with saturating activations like sigmoid or tanh, enabling effective training of deep networks.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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