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Exploratory Data Analysis →hardMultiple ChoiceObjective-mapped

MLS-C01 Exploratory Data Analysis Practice Question

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?

A
Area Under the ROC Curve (AUC-ROC)
AUC-ROC measures separability independent of class distribution.
B
Accuracy
Why wrong: Accuracy can be high even if the model always predicts the majority class.
C
F1 score
Why wrong: F1 score is used after model training.
D
Log loss
Why wrong: Log loss is for probabilistic predictions, not feature analysis.

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

✓

Area Under the ROC Curve (AUC-ROC)

AUC-ROC is the most appropriate metric for evaluating class separation in the feature space during EDA, especially with imbalanced data, because it measures the model's ability to distinguish between classes across all classification thresholds and is invariant to the proportion of positive and negative samples. Accuracy is misleading on highly imbalanced data (e.g., 99% accuracy would be trivial). F1 score is a model evaluation metric that combines precision and recall, but it is not directly suitable for assessing feature separability in EDA; it is typically used after model training. Log loss measures the probabilistic confidence of predictions but does not directly indicate class separation in the feature space.

Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.

Answer analysis

Option-by-option breakdown

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

Common exam trap: OSPF can fail even when IP connectivity looks correct

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

How to think about this question

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.

KKey Concepts to Remember

OSPF neighbours must agree on key parameters.
Router ID selection can affect neighbour relationships and LSDB output.
OSPF cost influences the preferred path.
A route can appear in OSPF information but not become the installed route.

TExam Day Tips

Check area mismatch first when OSPF adjacency fails.
Review passive interfaces when a network is advertised but no neighbour forms.
Use show ip ospf neighbor and show ip route clues carefully.

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

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. 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

Got this wrong? Here's your next step.

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.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Exploratory Data Analysis — This question tests Exploratory Data Analysis — OSPF neighbours must agree on key parameters..

What is the correct answer to this question?

The correct answer is: Area Under the ROC Curve (AUC-ROC) — AUC-ROC is the most appropriate metric for evaluating class separation in the feature space during EDA, especially with imbalanced data, because it measures the model's ability to distinguish between classes across all classification thresholds and is invariant to the proportion of positive and negative samples. Accuracy is misleading on highly imbalanced data (e.g., 99% accuracy would be trivial). F1 score is a model evaluation metric that combines precision and recall, but it is not directly suitable for assessing feature separability in EDA; it is typically used after model training. Log loss measures the probabilistic confidence of predictions but does not directly indicate class separation in the feature space.

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

What is the key concept behind this question?

OSPF neighbours must agree on key parameters.

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