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MLS-C01 Modeling Practice Question

Exhibit

Refer to the exhibit.
```
2019-11-01 10:23:45 Starting - Preparing the instances for training
2019-11-01 10:24:01 Downloading - Downloading input data
2019-11-01 10:24:15 Training - Training image download completed. Training in progress.
2019-11-01 10:25:30 Training - Training completed.
2019-11-01 10:25:31 Uploading - Uploading generated model artifacts
2019-11-01 10:25:35 Completed - Training job completed
```

Refer to the exhibit. A data scientist ran a SageMaker training job and reviewed the logs. The training completed quickly, but the model performance is very poor. What is the most likely cause?

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 training dataset is too small for the model complexity.

The training job completed very quickly (about 1 minute), which suggests the dataset is small. A small training dataset, especially relative to the model's complexity, leads to poor performance because the model cannot learn generalizable patterns. With insufficient data, the model may overfit the training samples or fail to converge to a good solution, resulting in poor test performance. Other options are less likely: overfitting (A) would typically show high training accuracy but poor validation accuracy, which is not indicated; data leakage (B) would artificially inflate performance; and a low learning rate (C) would cause slow convergence but not necessarily quick completion.

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 is overfitting to the training data.

    Why it's wrong here

    Overfitting would typically show high training accuracy but poor validation accuracy. The problem statement does not indicate such a scenario; instead, the training completed quickly with poor performance overall, which is more indicative of insufficient data.

  • ✗

    There is data leakage from the test set into the training set.

    Why it's wrong here

    Data leakage from the test set into the training set would artificially inflate performance, not reduce it. The poor performance suggests the opposite problem.

  • ✗

    The learning rate is too low, causing slow convergence.

    Why it's wrong here

    A low learning rate would cause slow convergence, meaning training would take longer, not complete quickly. The quick completion and poor performance point away from a low learning rate.

  • ✓

    The training dataset is too small for the model complexity.

    Why this is correct

    A small training dataset relative to model complexity leads to poor generalization. The model cannot learn meaningful patterns, resulting in poor performance. The quick training time also supports this.

About these practice questions

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JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This MLS-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 MLS-C01 exam.