AI0-001 AI Concepts and Techniques Practice Question
A machine learning engineer is training a transformer model for machine translation. The model's perplexity on the validation set is 8.5, and the BLEU score is 32. After increasing the number of encoder layers from 6 to 12, perplexity drops to 7.2 but BLEU decreases to 28. 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 model is overfitting the training data
Perplexity measures language model confidence, but BLEU measures translation quality. The deeper model may overfit to the training data, reducing perplexity but hurting generalization to validation translations. Overfitting causes high confidence (low perplexity) but poor translation diversity or exact matches.
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 the training data
Why this is correct
Overfitting leads to lower perplexity on validation (memorization) but worse generalization, reflected in the BLEU drop.
- ✗
The batch size is too small
Why it's wrong here
Batch size affects gradient noise but would not directly cause this perplexity-BLEU divergence pattern.
- ✗
The model is underfitting the training data
Why it's wrong here
Underfitting would cause high perplexity and low BLEU; here perplexity is low but BLEU dropped.
- ✗
The learning rate is too high
Why it's wrong here
A high learning rate would typically cause instability, not a drop in perplexity.
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Written by Johnson Ajibi, MSc IT Security
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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.