Courseiva
LLM FundamentalshardMultiple ChoiceObjective-mapped

1Z0-1127-25 LLM Fundamentals Practice Question

A developer is using OCI Generative AI for a question-answering system. The model frequently provides outdated information because the training data cutoff is over a year old. Which approach would most effectively address this issue?

⚠ Common exam trap

The 1Z0-1127 exam often tests the misconception that simply increasing model size or context length can solve knowledge staleness, when in fact only retrieval-based methods like RAG provide a scalable, real-time solution to keep answers current without retraining.

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

Implement a Retrieval-Augmented Generation (RAG) pipeline that retrieves up-to-date documents from an external knowledge base

Retrieval-Augmented Generation (RAG) directly addresses the problem of stale training data by dynamically retrieving current documents from an external knowledge base at inference time. This allows the model to generate answers grounded in up-to-date information without requiring retraining or a larger model, making it the most effective and practical solution for a question-answering system.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Implement a Retrieval-Augmented Generation (RAG) pipeline that retrieves up-to-date documents from an external knowledge base

    Why this is correct

    RAG allows the model to access current information dynamically, solving the cutoff problem.

  • Increase the context window to include more of the user's prompt

    Why it's wrong here

    Longer context does not provide new information; it only allows more of the user input.

  • Fine-tune the model on a dataset that includes recent information up to today

    Why it's wrong here

    Fine-tuning would require a dataset of recent data, and the model would still have a cutoff after that training; also fine-tuning is costly.

  • Switch to a larger model that has a more recent knowledge cutoff

    Why it's wrong here

    Larger models may have a slightly later cutoff but still not real-time; the cutoff is still a fixed date.

About these practice questions

Courseiva writes every 1Z0-1127-25 question from scratch — 768 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

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.