MLA-C01 ML Model Development Practice Question

A machine learning engineer trains a binary classifier in SageMaker and the model outputs class probabilities. The business requires that the model achieve at least 90% recall on the positive class, while keeping precision above 70%. The engineer uses the default threshold of 0.5 when deploying. Which approach should the engineer take to meet these requirements?

⚠ Common exam trap

The trap here is assuming that retraining or changing hyperparameters will automatically meet specific precision and recall targets, rather than adjusting the decision threshold.

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

✓

Perform a threshold analysis on the precision-recall curve and choose a threshold that yields recall ≥ 90% and precision > 70%.

The precision-recall curve illustrates how precision and recall vary with the decision threshold. To meet a recall target while keeping precision above a minimum, the engineer should evaluate the curve on a validation set and select a threshold that satisfies both constraints. This approach directly addresses the business requirement without retraining or using bias tools.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Perform a threshold analysis on the precision-recall curve and choose a threshold that yields recall ≥ 90% and precision > 70%.
Why this is correct
The precision-recall curve shows the trade-off between precision and recall at various thresholds. By analyzing this curve on a validation set, the engineer can identify a threshold that meets both the recall and precision requirements. This is the standard approach to select an operating point that aligns with business constraints, and it does not require retraining the model.
✗
Retrain the model with a higher learning rate and re-evaluate at the default threshold.
Why it's wrong here
Changing the learning rate alters model convergence but does not directly control the trade-off between precision and recall. The requirement is to meet a specific recall target while maintaining precision, which is achieved by adjusting the decision threshold, not by retraining with a different learning rate. Retraining may change the model's probability distribution unpredictably and does not guarantee the desired operating point.
✗
Use SageMaker Clarify to compute bias metrics and adjust the threshold accordingly.
Why it's wrong here
SageMaker Clarify is designed for bias detection and explainability, not for tuning the decision threshold to meet recall and precision targets. While Clarify can show disparate impact, it does not provide a direct mechanism to set a threshold that satisfies a recall constraint. The engineer needs a method to select an operating point on the precision-recall curve.
✗
Increase the number of epochs and use early stopping to improve both precision and recall.
Why it's wrong here
Increasing epochs may improve overall model performance but does not provide a direct way to satisfy a specific recall requirement while maintaining precision. Early stopping is used to prevent overfitting, not to adjust the decision threshold. The precision-recall trade-off is controlled by the threshold, not by the number of training epochs.

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MLA-C01 ML Model Development Practice Question

Why each option matters

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