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AI Concepts and TechniqueshardMultiple ChoiceObjective-mapped

AI0-001 AI Concepts and Techniques Practice Question

A deep learning engineer is training a transformer model and notices that validation perplexity increases after a few epochs while training perplexity continues to decrease. Which of the following is the MOST likely cause?

⚠ Common exam trap

CompTIA AI often tests the distinction between optimization issues (like learning rate or batch size) and generalization issues (like overfitting), and the trap here is that candidates may confuse a rising validation loss with a learning rate that is too high, when in fact the divergence between training and validation metrics is the definitive clue for overfitting.

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 is overfitting the training data

The described pattern—decreasing training perplexity alongside increasing validation perplexity—is the classic signature of overfitting. The model is memorizing the training data rather than learning generalizable patterns, causing its performance on unseen validation data to degrade after a certain point in 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.

  • The temperature parameter is set too high

    Why it's wrong here

    Temperature is used during generation, not training, and does not affect perplexity during training.

  • The batch size is too small

    Why it's wrong here

    Small batch size adds noise but doesn't typically cause validation loss to increase while training loss decreases.

  • The learning rate is too low

    Why it's wrong here

    A low learning rate typically causes slow convergence but not validation loss increase.

  • The model is overfitting the training data

    Why this is correct

    Overfitting leads to good training performance but poor generalisation, causing validation metrics to worsen.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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.