1Z0-1127-25 LLM Fundamentals Practice Question
An organization needs to deploy a model that can both understand and generate text, such as for a translation task where the input is in English and output is in French. Which model architecture is most suitable?
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
✓
Encoder-decoder (e.g., T5)
Encoder-decoder architectures like T5 are designed for sequence-to-sequence tasks. The encoder processes the input sequence, and the decoder generates the output sequence, making it ideal for translation.
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-decoder (e.g., T5)
Why this is correct
T5 is an encoder-decoder model specifically designed for text-to-text tasks like translation, where the full input is encoded and the decoder generates the output.
- ✗
Decoder-only (e.g., GPT)
Why it's wrong here
Decoder-only models like GPT can generate text but are less suited for tasks requiring explicit conditioning on a full input sequence; they can be used for translation but are not as optimal as encoder-decoder models.
- ✗
Encoder-only (e.g., BERT)
Why it's wrong here
Encoder-only models like BERT are designed for understanding tasks (classification, NER) and cannot generate text.
- ✗
Mixture of Experts (MoE)
Why it's wrong here
MoE is an architectural technique for scaling model size, not a specific architecture for sequence-to-sequence tasks.
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