- A
Collect more labeled data from other hospitals.
Why wrong: Time-consuming and may still be imbalanced.
- B
Use SMOTE to oversample the minority class in the training set.
SMOTE creates synthetic samples to balance classes.
- C
Use random undersampling of the majority class in the training set.
Why wrong: Undersampling loses data and may hurt performance.
- D
Switch to a deep neural network with more layers.
Why wrong: Deep learning may not improve on small dataset.
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 healthcare company is building a model to predict patient readmission within 30 days of discharge. The dataset includes 10,000 patient records with 200 features, including lab results, demographics, and historical admissions. The target variable is highly imbalanced: only 8% of patients are readmitted. The data scientist splits the data into 80% training and 20% test sets, ensuring the same proportion of readmissions in each. The scientist trains a logistic regression model and a random forest model. The logistic regression achieves 92% accuracy but recall of 10% for the readmitted class. The random forest achieves 90% accuracy but recall of 25%. The business requirement is to achieve at least 60% recall for readmissions while maintaining reasonable precision. The scientist also has access to a large collection of unlabeled patient records from other hospitals. Which strategy should the data scientist use to meet the business requirement?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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 SMOTE to oversample the minority class in the training set.
Option B is correct because using SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which can improve recall. Option A is wrong because collecting more data may not be feasible and may not help if imbalance persists. Option C is wrong because undersampling reduces data and may lose information. Option D is wrong because changing to a deep learning model may not help with limited data.
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
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more labeled data from other hospitals.
Why it's wrong here
Time-consuming and may still be imbalanced.
- ✓
Use SMOTE to oversample the minority class in the training set.
Why this is correct
SMOTE creates synthetic samples to balance classes.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Use random undersampling of the majority class in the training set.
Why it's wrong here
Undersampling loses data and may hurt performance.
- ✗
Switch to a deep neural network with more layers.
Why it's wrong here
Deep learning may not improve on small dataset.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
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
How to think about this question
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.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
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
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
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
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use SMOTE to oversample the minority class in the training set. — Option B is correct because using SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, which can improve recall. Option A is wrong because collecting more data may not be feasible and may not help if imbalance persists. Option C is wrong because undersampling reduces data and may lose information. Option D is wrong because changing to a deep learning model may not help with limited data.
What should I do if I get this MLS-C01 question wrong?
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.
Are there clue words in this question I should notice?
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
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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