Fundamentals of Large Language Models →hardMultiple SelectObjective-mapped

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

Which THREE factors should be considered when choosing between a fine-tuning and a prompt engineering approach?

⚠ Common exam trap

Oracle often tests the misconception that latency or model availability are primary differentiators, when in fact the core trade-off is between the need for deep personalization (fine-tuning) versus the ease and speed of prompt engineering, with labeled data and compute budget being practical constraints.

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

✓

Need for model personalization

Model personalization is a key driver for choosing fine-tuning over prompt engineering. Fine-tuning modifies the model's weights to adapt it to a specific domain or task, enabling deeper customization that prompt engineering alone cannot achieve, especially when the desired behavior requires learning new patterns or knowledge not present in the base model.

Answer analysis

Option-by-option breakdown

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

✗
Latency requirements
Why it's wrong here
Both approaches can achieve low latency; not a key differentiator.
✓
Need for model personalization
Why this is correct
Fine-tuning is necessary for deep personalization.
✗
Availability of foundation model in OCI
Why it's wrong here
OCI provides both fine-tuning and prompt engineering options.
✓
Amount of labeled data available
Why this is correct
Fine-tuning requires sufficient labeled data.
✓
Budget for GPU compute
Why this is correct
Fine-tuning is compute-intensive and costly.

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

Why each option matters

Option-by-option breakdown

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