Quick Answer
The correct answer is to use a weighted loss function during training. This technique directly addresses class imbalance by assigning a higher penalty to misclassifications of the minority class, forcing the model to pay more attention to the underrepresented 5% positive class rather than simply predicting the majority class for high accuracy. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how to handle imbalanced datasets without discarding data—common traps include choosing random under-sampling or removing the majority class, both of which lose valuable information, or using accuracy as a metric, which is misleading when classes are skewed. A key memory tip is to think of "weighted loss" as giving the minority class a louder voice during training, ensuring the model learns its patterns even when it appears rarely.
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?
Use a weighted loss function during training.
Weighted loss penalizes errors on minority class more heavily.
Use accuracy as the primary evaluation metric.
Why wrong: Accuracy can be misleading in imbalanced datasets.
Perform random under-sampling of the majority class.
Why wrong: Under-sampling may discard useful information.
Remove all examples from the majority class.
Why wrong: This severely reduces data and can lead to underfitting.
Answer choices
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 D is correct because using a weighted loss function during training assigns higher weight to the minority class, helping the model learn better from imbalanced data. Option A is wrong because removing the majority class reduces data size and may lose important patterns. Option B is wrong because random under-sampling can discard useful data. Option C is wrong because using accuracy as the evaluation metric is inappropriate for imbalanced data; precision/recall or AUC are better.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
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
Static NAT maps one inside address to one outside address.
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
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Static NAT maps one inside address to one outside address..
The correct answer is: Use a weighted loss function during training. — Option D is correct because using a weighted loss function during training assigns higher weight to the minority class, helping the model learn better from imbalanced data. Option A is wrong because removing the majority class reduces data size and may lose important patterns. Option B is wrong because random under-sampling can discard useful data. Option C is wrong because using accuracy as the evaluation metric is inappropriate for imbalanced data; precision/recall or AUC are better.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
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.
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