MLA-C01 ML Model Development Practice Question
A data scientist is evaluating a binary classification model for loan default prediction. Which THREE metrics should they consider to thoroughly assess model performance, especially for imbalanced classes?
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
✓
Recall
For imbalanced classification, accuracy can be misleading. AUC (Area under ROC curve) is robust to imbalance, F1 balances precision and recall, and recall (true positive rate) is important to catch defaults. RMSE and R² are for regression, NDCG is for ranking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
R²
Why it's wrong here
R² is for regression models.
- ✓
Recall
Why this is correct
Recall (true positive rate) is critical for default prediction to identify as many defaults as possible.
- ✗
RMSE
Why it's wrong here
RMSE is a regression metric, not suitable for classification.
- ✓
F1 score
Why this is correct
F1 score combines precision and recall, providing a balanced measure for imbalanced datasets.
- ✓
AUC
Why this is correct
AUC measures the model's ability to distinguish between classes and is insensitive to class imbalance.
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Same concept, more angles
3 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team wants to evaluate a binary classification model for credit risk. They need to understand the trade-off between false positives and false negatives. Which TWO metrics should they use? (Select TWO.)
medium- ✓ A.Recall
- ✓ B.Precision
- C.NDCG
- D.AUC-ROC
- E.RMSE
Why A: Precision and recall are complementary; precision measures false positives, recall measures false negatives. AUC-ROC summarizes the trade-off across thresholds. RMSE is for regression. NDCG is for ranking.
Variation 2. A data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing model performance?
medium- A.Precision
- B.Accuracy
- C.Recall
- ✓ D.AUC
Why D: AUC (Area Under the ROC Curve) is robust to class imbalance as it evaluates the model's ability to rank positive vs negative examples. Precision, recall, and F1 can be misleading if not threshold-optimized.
Variation 3. A data scientist is evaluating a binary classification model. They have the confusion matrix and want to assess the model's performance comprehensively. Which THREE metrics should they consider? (Select THREE.)
medium- ✓ A.Precision
- B.RMSE
- ✓ C.Recall
- ✓ D.F1 score
- E.R²
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.