Fundamentals of Large Language Models →easyMultiple SelectObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Which TWO are advantages of using retrieval-augmented generation (RAG) over fine-tuning for incorporating new knowledge?

⚠ Common exam trap

Oracle often tests the misconception that RAG is always faster or cheaper than fine-tuning, when in reality RAG introduces retrieval latency and higher token usage, making it less suitable for low-latency or high-throughput scenarios.

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

✓

Enables the model to access up-to-date information without retraining

RAG retrieves relevant, up-to-date information from an external knowledge base at inference time, allowing the model to answer questions about recent events or proprietary data without requiring any retraining. This is a key advantage over fine-tuning, which would need a new training cycle to incorporate the same new knowledge.

Answer analysis

Option-by-option breakdown

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

✗
Better at capturing domain-specific writing style
Why it's wrong here
Fine-tuning is better for style adaptation.
✓
Enables the model to access up-to-date information without retraining
Why this is correct
RAG retrieves fresh data from external sources.
✗
Eliminates the need for a vector database
Why it's wrong here
RAG often relies on a vector database for retrieval.
✗
Reduces token usage and latency compared to fine-tuning
Why it's wrong here
RAG adds retrieval step, which may increase latency.
✓
Cost-effective for large corpora that change frequently
Why this is correct
No need to retrain; only update the knowledge base.

Go deeper

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

Why each option matters

Option-by-option breakdown

Related to this question