Quick Answer
The answer is Area Under the ROC Curve (AUC). This metric is the correct choice for evaluating imbalanced classification models because it measures the model’s ability to distinguish between positive and negative classes across all classification thresholds, making it robust when one class dominates—like the 90% negative, 10% positive split in this scenario. Unlike accuracy, which can be misleadingly high at 0.85 simply by predicting the majority class, AUC captures true positive rate versus false positive rate, giving a fair assessment of performance on the minority positive class. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of metric selection for imbalanced data, a common trap where candidates gravitate toward accuracy or precision without considering class distribution. A useful memory tip: think of AUC as the “threshold-agnostic truth teller” for imbalanced datasets—it rewards models that rank positives higher than negatives, regardless of where you set the cutoff.
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 data scientist is using Amazon SageMaker Autopilot to automatically build a binary classification model. After the Autopilot job completes, the best model has an accuracy of 0.85 on the validation set. However, the data scientist notices a class imbalance (90% negative, 10% positive). Which metric should the data scientist use to evaluate the model's performance on the positive class?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Area Under the ROC Curve (AUC)
AUC is robust to class imbalance and evaluates overall ranking performance.
Recall
Why wrong: Recall alone does not consider false positives.
Accuracy
Why wrong: Accuracy can be high even if the model ignores the positive class due to imbalance.
Precision
Why wrong: Precision alone does not reflect how many positives are captured.
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)
Option D is correct because AUC measures the model's ability to distinguish between classes irrespective of threshold, and is suitable for imbalanced datasets. Option A is wrong because accuracy is misleading when classes are imbalanced. Option B is wrong because precision only considers positive predictions. Option C is wrong because recall only considers actual positives.
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)
Why this is correct
AUC is robust to class imbalance and evaluates overall ranking performance.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
OSPF neighbours must agree on key parameters.
Recall
Why it's wrong here
Recall alone does not consider false positives.
Accuracy
Why it's wrong here
Accuracy can be high even if the model ignores the positive class due to imbalance.
Precision
Why it's wrong here
Precision alone does not reflect how many positives are captured.
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.
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FAQ
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — OSPF neighbours must agree on key parameters..
The correct answer is: Area Under the ROC Curve (AUC) — Option D is correct because AUC measures the model's ability to distinguish between classes irrespective of threshold, and is suitable for imbalanced datasets. Option A is wrong because accuracy is misleading when classes are imbalanced. Option B is wrong because precision only considers positive predictions. Option C is wrong because recall only considers actual positives.
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.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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