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Machine Learning and Deep LearningmediumMultiple ChoiceObjective-mapped

AI0-001 Machine Learning and Deep Learning Practice Question

While training a deep neural network, the loss function fails to converge and oscillates wildly. Which adjustment is most likely to stabilize training?

⚠ Common exam trap

CompTIA often tests the misconception that increasing model complexity (more layers) or using more data (test set) directly fixes training instability, when in fact the learning rate is the primary culprit for oscillation and non-convergence.

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 learning rate

When the loss function oscillates wildly and fails to converge, it typically indicates that the learning rate is too high, causing the optimizer to overshoot the minima. Reducing the learning rate allows the gradient descent updates to take smaller, more stable steps, which helps the loss converge smoothly. This is a fundamental hyperparameter tuning step in deep learning training.

Answer analysis

Option-by-option breakdown

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

  • Increase the number of hidden layers

    Why it's wrong here

    More layers increase complexity and may worsen oscillations.

  • Decrease the batch size

    Why it's wrong here

    Smaller batch sizes introduce more noise, which can increase oscillations.

  • Reduce the learning rate

    Why this is correct

    Lower learning rate reduces step size, stabilizing training.

  • Use a test set

    Why it's wrong here

    Test set is for evaluation, not for training stability.

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This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.