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MLS-C01 Modeling Practice Question

A company is using Amazon SageMaker to train a time series forecasting model using the DeepAR algorithm. The training data contains multiple time series. The model is overfitting. Which action is LEAST likely to reduce overfitting?

⚠ Common exam trap

The trap here is that candidates mistakenly think reducing training data always reduces overfitting, but in time series forecasting with DeepAR, fewer time series actually weaken the cross-series learning that regularizes the model, making overfitting worse.

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

Reduce the number of time series in the training set.

Reducing the number of time series in the training set reduces the diversity of training data, which typically increases overfitting rather than reducing it. DeepAR relies on learning patterns across multiple related time series to generalize well; fewer time series mean less shared statistical strength, making the model more likely to memorize noise in the remaining series.

Answer analysis

Option-by-option breakdown

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

  • Decrease the number of layers in the neural network.

    Why it's wrong here

    Reducing model complexity reduces overfitting.

  • Increase the dropout rate.

    Why it's wrong here

    Dropout is a regularization technique.

  • Decrease the context length.

    Why it's wrong here

    Shorter context reduces model capacity.

  • Reduce the number of time series in the training set.

    Why this is correct

    Less data may worsen overfitting.

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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.