MLA-C01 ML Model Development Practice Question

A machine learning engineer is preparing a dataset for training a SageMaker built-in Linear Learner model for binary classification. The dataset contains a highly imbalanced target with only 2% positive examples. They want to improve the model's ability to detect positives without collecting more data. Which SageMaker Linear Learner hyperparameter should they adjust to assign more weight to the positive class?

⚠ Common exam trap

The trap here is assuming any class-weighting parameter works, when SageMaker Linear Learner specifically names it positive_example_weight_mult.

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

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positive_example_weight_mult

SageMaker Linear Learner provides positive_example_weight_mult to scale the contribution of positive examples in the loss. For a binary target with only 2% positives, increasing this multiplier pushes the model to prioritize positive class recall. Other hyperparameters like loss, mini_batch_size, or nonexistent balance_multiplier do not implement class weighting in this algorithm.

Answer analysis

Option-by-option breakdown

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

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loss
Why it's wrong here
The loss hyperparameter selects the objective function, such as logistic or hinge loss, but does not weight classes. Changing it alters the optimization target rather than compensating for class imbalance. In the described scenario, the engineer needs to upweight positives, not switch loss functions. Selecting a different loss would not address the 2% positive rate and could even worsen positive detection.
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mini_batch_size
Why it's wrong here
mini_batch_size controls how many examples are processed per gradient update. It affects training stability and speed but not the relative weighting of positive versus negative examples. In an imbalanced binary classification scenario, adjusting batch size does not increase the model's sensitivity to the minority class, so it would not meet the requirement of improving positive detection without more data.
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positive_example_weight_mult
Why this is correct
SageMaker Linear Learner includes positive_example_weight_mult, which multiplies the weight of positive examples during training. For imbalanced binary classification, setting this above 1 increases the loss contribution of positives, encouraging the model to detect them better. This directly addresses the scenario without resampling or collecting more data, and it is a documented hyperparameter of the built-in Linear Learner algorithm.
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balance_multiplier
Why it's wrong here
balance_multiplier is not a valid SageMaker Linear Learner hyperparameter. While some frameworks offer class weighting, the built-in Linear Learner uses positive_example_weight_mult for this purpose. Specifying a nonexistent parameter would cause job failure or be ignored, and it would not achieve the desired weighting of positive examples in the imbalanced binary classification scenario.

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MLA-C01 ML Model Development Practice Question

Why each option matters

Option-by-option breakdown

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