1Z0-1127-25 Mixture of Experts (MoE) Practice Question
A practitioner needs to choose a pre-trained model for a sentiment analysis task on customer reviews. The model must be efficient for inference and capable of handling multiple languages. Which architecture is MOST suitable?
⚠ Common exam trap
Candidates may default to BERT as the standard for classification, but MoE offers better efficiency and multilingual support in modern architectures.
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
✓
Mixture of Experts model
Mixture of Experts (MoE) models are designed for efficient inference by activating only a subset of parameters per input, reducing computational cost. They can also support multiple languages by allocating different experts to different language patterns, making them highly suitable for multilingual sentiment analysis. In contrast, encoder-only BERT models are efficient but may not scale as well for multilingual tasks without large capacity, encoder-decoder models are optimized for sequence-to-sequence tasks, and decoder-only models are primarily generative and less efficient for classification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Encoder-only BERT model
Why it's wrong here
Encoder-only models like BERT are strong for classification but may be less efficient for multilingual inference due to full attention computation, and they are not specifically optimized for multi-language handling in an efficient manner.
- ✗
Encoder-decoder T5 model
Why it's wrong here
Encoder-decoder models like T5 are designed for sequence-to-sequence tasks, making them less efficient for simple classification due to the additional decoding step.
- ✗
Decoder-only GPT model
Why it's wrong here
Decoder-only models like GPT are generative and typically slower for classification tasks, and their unidirectional attention may miss context.
- ✓
Mixture of Experts model
Why this is correct
Mixture of Experts models achieve efficiency through sparse activation and can specialize experts per language, making them ideal for multilingual sentiment analysis with fast inference.
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