1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An OCI GenAI practitioner wants to deploy a model that can generate code from natural language descriptions. Which type of model is most suitable?
⚠ Common exam trap
OCI GenAI exams often test the distinction between encoder-only (BERT) and decoder-only (GPT) architectures, leading candidates to mistakenly choose BERT for generation tasks because they associate it with language understanding, not realizing it cannot generate sequences.
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
✓
GPT
GPT (Generative Pre-trained Transformer) is the most suitable model for code generation from natural language because it is an autoregressive language model optimized for text generation tasks. Unlike encoder-only models, GPT generates coherent, contextually relevant sequences of tokens, making it ideal for producing code based on descriptive prompts.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
T5
Why it's wrong here
T5 can generate text but is less commonly used for code generation compared to GPT.
- ✗
ResNet
Why it's wrong here
ResNet is a convolutional neural network for image processing, not text generation.
- ✗
BERT
Why it's wrong here
BERT is an encoder-only model used for understanding, not generation.
- ✓
GPT
Why this is correct
GPT (decoder-only) excels at autoregressive text generation, ideal for code generation.
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