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Question 848 of 1,755

Modeling →hardMultiple SelectObjective-mapped

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 machine learning team is building a multi-class image classifier using a pre-trained ResNet-50 model in Amazon SageMaker. The dataset has 10 classes but is highly imbalanced, with one class representing 80% of the samples. The team wants to improve model performance on the minority classes. Which TWO of the following approaches are most likely to help? (Select TWO.)

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.

Question 1hardmulti select

Full question →

A
Oversample the minority classes in the training data.
Oversampling increases representation of minority classes, balancing the training set.
B
Reduce the batch size to increase the frequency of weight updates.
Why wrong: Batch size affects training dynamics but does not directly address class imbalance.
C
Increase the number of layers in the model.
Why wrong: Adding layers increases model capacity but does not directly address imbalance and may lead to overfitting.
D
Switch to a focal loss function.
Why wrong: Focal loss is designed for single-stage object detectors and is less commonly used in multi-class classification with pre-trained models; class weighting is more direct.
E
Use class weighting in the loss function.
Class weighting penalizes misclassifications of minority classes more heavily, which helps the model learn from them.

Full breakdown with real-world context →

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

✓

Oversample the minority classes in the training data.

Oversampling the minority classes (Option A) directly addresses class imbalance by replicating samples from underrepresented classes, giving the model more exposure to them during training. This is a standard data-level technique that helps the ResNet-50 model learn discriminative features for minority classes without altering the loss function or model architecture.

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.

✓
Oversample the minority classes in the training data.
Why this is correct
Oversampling increases representation of minority classes, balancing the training set.
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.
✗
Reduce the batch size to increase the frequency of weight updates.
Why it's wrong here
Batch size affects training dynamics but does not directly address class imbalance.
✗
Increase the number of layers in the model.
Why it's wrong here
Adding layers increases model capacity but does not directly address imbalance and may lead to overfitting.
✗
Switch to a focal loss function.
Why it's wrong here
Focal loss is designed for single-stage object detectors and is less commonly used in multi-class classification with pre-trained models; class weighting is more direct.
✓
Use class weighting in the loss function.
Why this is correct
Class weighting penalizes misclassifications of minority classes more heavily, which helps the model learn from them.
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.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may incorrectly select focal loss (Option D) as a standalone answer, but the question requires exactly two correct options, and class weighting (Option E) is a more straightforward loss-modification technique that is explicitly tested in the MLS-C01 exam as a standard approach for imbalanced classification.

Detailed technical explanation

How to think about this question

Oversampling can be implemented via libraries like imbalanced-learn (e.g., RandomOverSampler) or by duplicating minority samples in the SageMaker training pipeline. Class weighting (Option E) modifies the cross-entropy loss by assigning higher weights to minority classes, effectively penalizing misclassifications more heavily; in PyTorch, this is done via the `weight` parameter in `nn.CrossEntropyLoss`. Both techniques are complementary and often used together in practice to combat severe imbalance like the 80/10/10/... split described.

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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

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.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

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: Oversample the minority classes in the training data. — Oversampling the minority classes (Option A) directly addresses class imbalance by replicating samples from underrepresented classes, giving the model more exposure to them during training. This is a standard data-level technique that helps the ResNet-50 model learn discriminative features for minority classes without altering the loss function or model architecture.

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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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.