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Question 248 of 1,755

Modeling →hardMultiple ChoiceObjective-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 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.

A
Apply PCA to reduce dimensionality and noise.
Why wrong: PCA may discard important information for the minority class.
B
Use the unlabeled data for semi-supervised learning with pseudo-labeling.
Pseudo-labeling leverages unlabeled data to improve minority class detection.
C
Increase the learning rate to accelerate convergence.
Why wrong: Higher learning rate may not help learning minority class.
D
Reduce the number of features using feature selection to simplify the model.
Why wrong: Feature selection may not address the imbalance.

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 the unlabeled data for semi-supervised learning with pseudo-labeling.

Option B is correct because using unlabeled data for pseudo-labeling can help the model learn patterns of the minority class by generating additional training examples, which is especially beneficial when the labeled dataset is imbalanced. Option A is incorrect because PCA reduces dimensionality but does not address class imbalance and may discard features important for the default class. Option C is incorrect because increasing the learning rate can cause the model to overshoot optimal minima and may lead to overfitting, not improving recall and precision for the minority class. Option D is incorrect because feature selection reduces the number of features but does not directly address class imbalance; it could even remove features that are relevant for predicting defaults.

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.

✗
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
Read the scenario before looking for a memorised answer.
✗
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: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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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: Use the unlabeled data for semi-supervised learning with pseudo-labeling. — Option B is correct because using unlabeled data for pseudo-labeling can help the model learn patterns of the minority class by generating additional training examples, which is especially beneficial when the labeled dataset is imbalanced. Option A is incorrect because PCA reduces dimensionality but does not address class imbalance and may discard features important for the default class. Option C is incorrect because increasing the learning rate can cause the model to overshoot optimal minima and may lead to overfitting, not improving recall and precision for the minority class. Option D is incorrect because feature selection reduces the number of features but does not directly address class imbalance; it could even remove features that are relevant for predicting defaults.

What should I do if I get this MLS-C01 question wrong?

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?

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.