easyMultiple Choice
MLA-C01 Practice Question: Refer to the exhibit
Exhibit
Model Artifacts:
ModelArtifacts:
S3ModelArtifacts: s3://my-bucket/output/model.tar.gz
ModelMetrics:
Metrics:
training:accuracy: 0.95
validation:accuracy: 0.92
FinalHyperParameters:
learning_rate: 0.01
batch_size: 32
epochs: 10Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accuracy and 92% validation accuracy. Which statement is true?
⚠ Common exam trap
MLA-C01 often tests the overfitting heuristic — candidates see 'training > validation' and reflexively pick 'overfitting,' but the exam expects recognition that a small gap with high accuracy is acceptable.
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
✓
The model has acceptable performance with a small generalization gap
Training accuracy of 95% and validation accuracy of 92% indicate the model performs well on both seen and unseen data, with only a 3-point generalization gap. This is a healthy, acceptable result — the model is neither severely overfitting nor underfitting. The small gap suggests good generalization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model has acceptable performance with a small generalization gap
Why this is correct
Training accuracy of 95% against validation accuracy of 92% shows only a three-point gap, indicating the model generalises well without severe overfitting. This satisfies the acceptable-performance criterion, since both metrics are high and closely aligned.
- ✗
The model is underfitting because the validation accuracy is too low
Why it's wrong here
Underfitting means both training and validation accuracy are low, indicating the model has not learned the underlying pattern. Here 95% training accuracy shows the model fits the data well. The option is tempting because 92% validation accuracy sounds low in absolute terms, but it is close to training performance.
- ✗
The model needs more epochs to improve validation accuracy
Why it's wrong here
A 95% versus 92% split shows the model already converging, so adding epochs risks overfitting rather than lifting validation accuracy. The option is tempting because more training often helps early in a run. Without a learning curve showing validation accuracy still rising, more epochs are not indicated.
- ✗
The model is overfitting because the training accuracy is higher than validation accuracy
Why it's wrong here
A three-point gap between training and validation accuracy is normal generalisation loss, not overfitting; overfitting shows a large gap with validation accuracy plateauing or degrading. The option is tempting because any gap superficially suggests memorisation. The stated accuracies indicate the model generalises acceptably.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.