1Z0-1127-25 LLM Fundamentals Practice Question
Which model architecture is used by BERT for natural language understanding tasks?
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-only
BERT uses an encoder-only architecture, which processes the entire input sequence bidirectionally. This makes it well-suited for tasks like classification, NER, and QA where understanding the full context is important.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Recurrent neural network
Why it's wrong here
BERT is based on the Transformer architecture, not RNNs.
- ✗
Encoder-decoder
Why it's wrong here
Encoder-decoder models (e.g., T5) are used for sequence-to-sequence tasks like translation.
- ✓
Encoder-only
Why this is correct
BERT is an encoder-only model that uses bidirectional self-attention to understand the full context of the input.
- ✗
Decoder-only
Why it's wrong here
Decoder-only models (e.g., GPT) are used for text generation, not understanding tasks like BERT.
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