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AI Concepts and FoundationsmediumMultiple ChoiceObjective-mapped

AI0-001 AI Concepts and Foundations Practice Question

An AI engineer is tuning a deep learning model and observes that the training loss decreases very slowly. The learning rate is set to 0.001. Which adjustment is most likely to speed up convergence?

⚠ Common exam trap

CompTIA often tests the misconception that decreasing the learning rate always improves training, when in fact a learning rate that is too low is a primary cause of slow convergence, and the correct adjustment is to increase it within a safe range.

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

Increase the learning rate to 0.01

A learning rate of 0.001 is causing the model to take very small steps toward the minimum of the loss function, resulting in slow convergence. Increasing the learning rate to 0.01 allows larger weight updates per iteration, which typically speeds up training. However, care must be taken not to overshoot the optimum, as an excessively high learning rate can cause divergence.

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 learning rate to 0.01

    Why this is correct

    A higher learning rate allows larger weight updates, potentially speeding up convergence.

  • Add more hidden layers

    Why it's wrong here

    Adding layers increases complexity and training time, not necessarily speeding convergence.

  • Decrease the learning rate to 0.0001

    Why it's wrong here

    A lower learning rate will slow convergence further.

  • Increase the batch size

    Why it's wrong here

    Larger batch sizes can cause slower per-update convergence and require more epochs.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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