The answer is to use the unlabeled data for semi-supervised learning with pseudo-labeling. This is correct because semi-supervised learning to improve minority class recall directly addresses the core issue: the model lacks sufficient representative patterns of the default class due to class imbalance, and pseudo-labeling leverages the large pool of unlabeled transactions to iteratively augment the training set with high-confidence predictions, forcing the model to learn the minority class boundaries more effectively. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to handle imbalanced datasets when simple threshold tuning fails—a common trap is to jump to feature reduction or PCA, but those ignore the fundamental data scarcity problem. The key insight is that unlabeled data can compensate for a small minority class when the model’s confidence is used to generate pseudo-labels. Memory tip: “When threshold fails and data is vast, pseudo-label the minority class fast.”
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 financial services company uses Amazon SageMaker to train a model for credit risk prediction. The dataset contains 500 features and 1 million records. The target variable is binary with 20% default rate. The data scientist uses a gradient boosting algorithm (XGBoost) with default hyperparameters. After training, the model achieves 95% accuracy, but the precision for the default class is only 30%, and recall is 15%. The business requires at least 50% recall and 40% precision for the default class. The data scientist tries to adjust the decision threshold, but this does not simultaneously meet both targets. The scientist suspects that the model is not learning the default patterns well. The company also has a large dataset of unlabeled transactions that could be used. Which action should the data scientist take to improve the model?
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the unlabeled data for semi-supervised learning with pseudo-labeling.
Option C is correct because using the unlabeled data for pseudo-labeling can improve the model when the labeled dataset is imbalanced and the model is struggling to learn the minority class. Option A is wrong because reducing the number of features may not help if the features are relevant. Option B is wrong because increasing the learning rate may cause overfitting or divergence. Option D is wrong because PCA may discard valuable information.
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.
✗
Apply PCA to reduce dimensionality and noise.
Why it's wrong here
PCA may discard important information for the minority class.
✓
Use the unlabeled data for semi-supervised learning with pseudo-labeling.
Why this is correct
Pseudo-labeling leverages unlabeled data to improve minority class detection.
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.
✗
Increase the learning rate to accelerate convergence.
Why it's wrong here
Higher learning rate may not help learning minority class.
✗
Reduce the number of features using feature selection to simplify the model.
Why it's wrong here
Feature selection may not address the imbalance.
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 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
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.
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 the unlabeled data for semi-supervised learning with pseudo-labeling. — Option C is correct because using the unlabeled data for pseudo-labeling can improve the model when the labeled dataset is imbalanced and the model is struggling to learn the minority class. Option A is wrong because reducing the number of features may not help if the features are relevant. Option B is wrong because increasing the learning rate may cause overfitting or divergence. Option D is wrong because PCA may discard valuable information.
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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Question Discussion
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