1Z0-1127-25 LLM Fundamentals Practice Question
An organization wants to deploy a model that can summarize long financial reports (5000+ tokens) without losing context. Which model architecture is best suited for this requirement?
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 model (e.g., T5)
Encoder-decoder models like T5 or BART are designed for sequence-to-sequence tasks such as summarization, and can handle long inputs with their encoder.
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 model (e.g., T5)
Why this is correct
Encoder-decoder architecture excels at summarization and can handle long inputs via the encoder.
- ✗
Mixture-of-experts model
Why it's wrong here
MoE is a scaling technique, not specifically designed for summarization.
- ✗
Decoder-only model (e.g., GPT)
Why it's wrong here
Decoder-only models can generate text but often have limited context windows and are less efficient for summarization.
- ✗
Encoder-only model (e.g., BERT)
Why it's wrong here
BERT is bidirectional but not designed for text generation tasks.
Go deeper
Related to this question
About these practice questions
One of 768 original 1Z0-1127-25 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This 1Z0-1127-25 practice question is part of Courseiva's free Oracle 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 1Z0-1127-25 exam.