Quick Answer
The answer is the Area Under the ROC Curve (AUC-ROC). This metric is the most appropriate for evaluating class separation in imbalanced data because it measures how well the model distinguishes between the positive and negative classes across all classification thresholds, making it robust to the severe skew of a 1% positive class. Unlike accuracy, which can be misleadingly high by simply predicting the majority class, AUC-ROC focuses on separability in the feature space, directly assessing whether the distributions of the two classes are distinct. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that AUC-ROC is a threshold-independent metric ideal for EDA on imbalanced datasets, while F1 score and log loss are better suited for model evaluation after training. A common trap is choosing accuracy or F1 score, but remember: for raw class separation during exploratory analysis, AUC-ROC is your go-to. Memory tip: “AUC for separation, not for prediction.”
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is analyzing a dataset where the target variable is highly imbalanced (1% positive class). They are performing EDA. Which metric is most appropriate for evaluating class separation in the feature space?
Area Under the ROC Curve (AUC-ROC)
AUC-ROC measures separability independent of class distribution.
Accuracy
Why wrong: Accuracy can be high even if the model always predicts the majority class.
F1 score
Why wrong: F1 score is used after model training.
Log loss
Why wrong: Log loss is for probabilistic predictions, not feature analysis.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Area Under the ROC Curve (AUC-ROC)
Option D is correct because AUC-ROC is robust to class imbalance and measures separability. Option A is wrong because accuracy is misleading on imbalanced data. Option B is wrong because F1 score is a model evaluation metric, not for EDA. Option C is wrong because log loss is a probabilistic metric.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Area Under the ROC Curve (AUC-ROC)
Why this is correct
AUC-ROC measures separability independent of class distribution.
Related concept
OSPF neighbours must agree on key parameters.
Accuracy
Why it's wrong here
Accuracy can be high even if the model always predicts the majority class.
F1 score
Why it's wrong here
F1 score is used after model training.
Log loss
Why it's wrong here
Log loss is for probabilistic predictions, not feature analysis.
Common exam traps
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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 OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — OSPF neighbours must agree on key parameters..
The correct answer is: Area Under the ROC Curve (AUC-ROC) — Option D is correct because AUC-ROC is robust to class imbalance and measures separability. Option A is wrong because accuracy is misleading on imbalanced data. Option B is wrong because F1 score is a model evaluation metric, not for EDA. Option C is wrong because log loss is a probabilistic metric.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
OSPF neighbours must agree on key parameters.
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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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