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Machine Learning Implementation and Operations →easyMultiple ChoiceObjective-mapped

MLS-C01 Practice Question: Machine Learning Implementation and Operations

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company is using Amazon SageMaker to build a binary classification model. The dataset is highly imbalanced, with 95% negative class and 5% positive class. Which technique should be used to address the class imbalance?

A
Use a weighted loss function during training.
Weighted loss penalizes errors on minority class more heavily.
B
Use accuracy as the primary evaluation metric.
Why wrong: Accuracy can be misleading in imbalanced datasets.
C
Perform random under-sampling of the majority class.
Why wrong: Under-sampling may discard useful information.
D
Remove all examples from the majority class.
Why wrong: This severely reduces data and can lead to underfitting.

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

✓

Use a weighted loss function during training.

Option A is correct because using a weighted loss function (e.g., class weights in SageMaker's built-in XGBoost or custom PyTorch loss) assigns a higher penalty to misclassifications of the minority positive class. This directly addresses the 95:5 imbalance by making the model more sensitive to the positive class during gradient updates, without discarding data.

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.

✓
Use a weighted loss function during training.
Why this is correct
Weighted loss penalizes errors on minority class more heavily.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use accuracy as the primary evaluation metric.
Why it's wrong here
Accuracy can be misleading in imbalanced datasets.
✗
Perform random under-sampling of the majority class.
Why it's wrong here
Under-sampling may discard useful information.
✗
Remove all examples from the majority class.
Why it's wrong here
This severely reduces data and can lead to underfitting.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often choose under-sampling (Option C) as a quick fix, but the exam tests understanding that under-sampling discards data and can hurt performance, while weighted loss preserves all data and is the preferred technique in SageMaker for imbalanced classification.

Detailed technical explanation

How to think about this question

In SageMaker, weighted loss functions can be implemented via the `scale_pos_weight` parameter in XGBoost (set to ratio of negative to positive samples, e.g., 19:1) or by passing class weights to a custom PyTorch `CrossEntropyLoss`. Under the hood, the loss gradient is scaled per sample, so minority class errors dominate the weight update, effectively rebalancing the learning signal without altering the dataset distribution.

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?

Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use a weighted loss function during training. — Option A is correct because using a weighted loss function (e.g., class weights in SageMaker's built-in XGBoost or custom PyTorch loss) assigns a higher penalty to misclassifications of the minority positive class. This directly addresses the 95:5 imbalance by making the model more sensitive to the positive class during gradient updates, without discarding data.

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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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.