MLA-C01 Practice Question: Refer to the exhibit

Exhibit

[1] #011train-auc:0.890
[2] #011train-auc:0.895
[3] #011train-auc:0.892
[4] #011validation-auc:0.880

Refer to the exhibit. A SageMaker training job logs show training AUC increasing but validation AUC plateauing at 0.880. What is the most likely issue?

⚠ Common exam trap

The AWS ML Engineer Associate exam often tests the distinction between overfitting and underfitting by showing a divergence in training vs. validation metrics, where candidates mistakenly attribute the plateau to a learning rate issue or insufficient data rather than recognizing the hallmark of overfitting.

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

✓

Overfitting

The training AUC increasing while validation AUC plateaus at 0.880 is a classic sign of overfitting. The model is learning noise and patterns specific to the training data that do not generalize to unseen validation data, causing the validation metric to stall despite continued improvement on the training set.

Answer analysis

Option-by-option breakdown

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

✓
Overfitting
Why this is correct
Training AUC keeps rising while validation AUC plateaus at 0.880, meaning the model fits training noise rather than generalisable patterns. This divergence between training and validation curves is the classic signature of overfitting, satisfying the stem's observed symptom.
✗
Learning rate too high
Why it's wrong here
A high learning rate causes erratic loss or divergence, not steadily rising training AUC with a plateaued validation curve. That pattern indicates the model is memorising training data. A high learning rate would be the suspect when training loss oscillates or fails to decrease at all.
✗
Underfitting
Why it's wrong here
Underfitting produces low training AUC alongside low validation AUC, since the model cannot capture the underlying pattern. Here training AUC keeps climbing, which is the opposite signature. Underfitting would be the diagnosis when both curves plateau early at a poor value.
✗
Insufficient training data
Why it's wrong here
Insufficient training data typically depresses both training and validation performance, or causes high variance across folds. The stem shows training AUC still increasing, meaning the model fits the data it has. Data scarcity would be indicated by poor training AUC despite adequate model capacity.

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MLA-C01 Practice Question: Refer to the exhibit

Exhibit

Why each option matters

Option-by-option breakdown

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