Generative AI Leader Fundamentals of Generative AI Practice Question
Which THREE factors should be considered when choosing between fine-tuning and prompt engineering for a generative AI task? (Choose three.)
⚠ Common exam trap
Google Cloud often tests the misconception that cost or model size are primary decision factors, when in reality the core trade-off is between data availability (labeled vs. unlabeled) and the degree of task specialization required.
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
✓
Availability of labeled training data
Fine-tuning requires a labeled dataset specific to the target task to adjust model weights via supervised learning, whereas prompt engineering relies on the model's existing knowledge without additional training data. Without sufficient labeled data, prompt engineering is often the only viable approach, as fine-tuning would risk overfitting or poor generalization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Availability of labeled training data
Why this is correct
Fine-tuning needs labeled data.
- ✗
Cost of API calls per request
Why it's wrong here
Prompt engineering is cheaper per request.
- ✓
Latency requirements for the application
Why this is correct
Fine-tuning can reduce latency for specific tasks.
- ✓
Degree of task specialization required
Why this is correct
Fine-tuning is better for specialized tasks.
- ✗
Size of the base model
Why it's wrong here
Model size is not a primary factor.
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