AI0-001 AI Concepts and Foundations Practice Question
A company built a speech-to-text model using a recurrent neural network (RNN). During deployment, the model performs poorly on accented speech. Which action would most effectively improve model robustness?
⚠ Common exam trap
CompTIA often tests the misconception that architectural changes (like switching to CNN or adding regularization) can fix data distribution mismatches, when the real solution is to address the missing data diversity through augmentation or retraining.
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
✓
Augment the training dataset with various accented audio samples and retrain the model.
Augmenting the training dataset with diverse accented audio samples directly addresses the root cause of poor performance—distribution shift between training and deployment data. Retraining the model on this enriched dataset allows the RNN to learn invariant features across accents, improving generalization without altering the model architecture or risking catastrophic forgetting from fine-tuning on a tiny sample.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect a small sample of accented speech and fine-tune the model on that sample only.
Why it's wrong here
Fine-tuning on a small sample may lead to catastrophic forgetting or overfitting.
- ✗
Add dropout and reduce the number of RNN layers to prevent overfitting to the current data.
Why it's wrong here
This may reduce overfitting but does not address the lack of accent diversity.
- ✓
Augment the training dataset with various accented audio samples and retrain the model.
Why this is correct
Data augmentation with accents directly addresses the performance gap.
- ✗
Replace the RNN with a convolutional neural network (CNN) for feature extraction.
Why it's wrong here
While CNNs can help, the fundamental issue is data diversity, not architecture.
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JA
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.