Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Which THREE factors should be considered when choosing between a fine-tuned model and a prompted foundation model for a generative AI solution? (Select 3)
⚠ Common exam trap
Google Cloud often tests the misconception that inference latency is a deciding factor between fine-tuning and prompting, when in reality both can be optimized for speed, and the key differentiators are data availability, domain specificity, and cost per token.
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 domain-specific vocabulary
Fine-tuning allows the model to learn domain-specific vocabulary and terminology that may not be well-represented in the foundation model's pre-training data. This is critical for specialized fields like legal, medical, or technical domains where precise language is required for accurate outputs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Need for domain-specific vocabulary
Why this is correct
Fine-tuning can incorporate domain language.
- ✗
Inference latency requirements
Why it's wrong here
Latency is similar; not a deciding factor.
- ✓
Size of training data available
Why this is correct
Fine-tuning requires substantial data.
- ✗
Whether the model is open-source
Why it's wrong here
Open-source status does not determine approach.
- ✓
Token cost per request
Why this is correct
Fine-tuned models may have lower per-token cost.
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