The answer is to replace cross-entropy loss with focal loss. Focal loss is specifically designed to address class imbalance in medical image classification by down-weighting the loss contribution from well-classified majority class examples, forcing the model to focus on hard, misclassified minority examples—directly improving recall on the rarest class. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of loss functions for imbalanced datasets, often appearing in scenarios where data augmentation alone fails to boost minority class performance. A common trap is assuming oversampling fixes all imbalance, but focal loss targets the gradient flow itself. Remember the mnemonic: “Focal flips the focus—from easy majority to hard minority.”
MLS-C01 Modeling Practice Question
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 research team is developing a deep learning model to classify medical images into 10 disease categories. They have a dataset of 50,000 labeled images, but the class distribution is highly imbalanced: the most common class has 20,000 images, while the rarest class has only 200 images. To address this, they apply data augmentation (random rotations, flips, and brightness adjustments) to the minority classes until each class has 20,000 images. They then train a convolutional neural network (CNN) from scratch using cross-entropy loss. The model achieves 95% overall accuracy but only 30% recall on the rarest class. Which change is MOST likely to improve recall on the rarest class without significantly reducing overall accuracy?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Replace cross-entropy loss with focal loss
Focal loss is specifically designed to address class imbalance by down-weighting the loss contribution from well-classified examples (majority classes) and focusing training on hard, misclassified examples (minority classes). This directly improves recall on the rarest class, while cross-entropy loss treats all classes equally, causing the model to be biased toward the majority classes.
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 dropout rate from 0.2 to 0.5 to reduce overfitting
Why it's wrong here
Higher dropout prevents overfitting but does not address class imbalance directly.
✓
Replace cross-entropy loss with focal loss
Why this is correct
Focal loss reduces the loss contribution from easy examples and focuses on hard, minority examples, improving recall.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch from Adam optimizer to SGD with momentum
Why it's wrong here
Optimizer choice affects convergence speed but does not directly address class imbalance.
✗
Reduce the batch size from 64 to 16 to increase stochasticity
Why it's wrong here
Smaller batch size can help escape local minima but does not specifically improve recall on rare classes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between regularization techniques (dropout, batch size) and loss function modifications (focal loss) for class imbalance, trapping candidates who think overfitting is the primary issue when the real problem is the model's bias toward majority classes.
Detailed technical explanation
How to think about this question
Focal loss introduces a modulating factor (1 - p_t)^γ to the standard cross-entropy loss, where p_t is the model's estimated probability for the true class and γ (typically 2) is a focusing parameter. This reduces the loss for well-classified examples (high p_t) and increases the loss for misclassified ones (low p_t), effectively forcing the model to pay more attention to the rare class. In practice, focal loss is often used with a class-balanced variant (e.g., adding α weighting) to further stabilize training on extreme imbalances like the 200 vs. 20,000 example scenario.
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: Replace cross-entropy loss with focal loss — Focal loss is specifically designed to address class imbalance by down-weighting the loss contribution from well-classified examples (majority classes) and focusing training on hard, misclassified examples (minority classes). This directly improves recall on the rarest class, while cross-entropy loss treats all classes equally, causing the model to be biased toward the majority classes.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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