MLA-C01 ML Model Development Practice Question
A data scientist is using SageMaker built-in XGBoost algorithm for a binary classification task. Which objective metric is MOST appropriate for SageMaker Automatic Model Tuning to maximize?
⚠ Common exam trap
The trap is mixing regression metrics (MAE, RMSE) and ranking metrics (NDCG) into a binary classification question — candidates who do not map metric families to task types will pick a plausible-sounding but wrong objective.
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
✓
validation:auc
For binary classification, AUC (Area Under the ROC Curve) is the standard evaluation metric because it measures the model's ability to discriminate between the two classes across all classification thresholds, independent of class balance. SageMaker's built-in XGBoost exposes validation:auc as the objective metric for binary classification tuning. MAE and RMSE are regression metrics, and NDCG is a ranking metric, so none of them fit binary classification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
validation:mae
Why it's wrong here
validation:mae measures mean absolute error for regression, so maximising it is meaningless for binary classification. It is tempting because MAE is a standard XGBoost metric, but classification requires validation:auc, which the algorithm reports for binary:logistic objectives.
- ✗
validation:rmse
Why it's wrong here
RMSE measures regression error magnitude, not binary classification quality, so maximising it optimises the wrong quantity. It is tempting because XGBoost supports regression objectives, but validation:rmse would be correct for a regression task, whereas binary classification needs validation:auc or validation:logloss.
- ✗
validation:ndcg
Why it's wrong here
Validation:ndcg measures ranking quality, so it cannot evaluate binary classification labels and gives no usable signal for tuning. It is tempting because NDCG is the standard objective for learning-to-rank jobs, such as search or recommendation models where ordered relevance grades matter, and would be the right choice there.
- ✓
validation:auc
Why this is correct
For binary classification, `validation:auc` maximises the area under the ROC curve, giving a threshold-independent measure of class separation. This satisfies the stem's requirement for the most appropriate tuning objective, since AUC handles imbalanced binary labels better than accuracy and is natively supported by SageMaker Automatic Model Tuning.
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