1Z0-1127-25 Using OCI Generative AI Service Practice Question
A financial firm wants to use OCI Generative AI for contract analysis. They need to reduce costs by using a smaller, specialized model. Which approach should they take?
⚠ Common exam trap
Oracle often tests the misconception that larger models are always better for specialized tasks, but the trap here is that fine-tuning a smaller model on a dedicated AI cluster provides both cost efficiency and domain accuracy, which candidates overlook in favor of familiar large-model options.
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
✓
Fine-tune a smaller base model on a dedicated AI cluster
Fine-tuning a smaller base model on a dedicated AI cluster allows the financial firm to tailor the model specifically for contract analysis tasks, reducing computational overhead and cost compared to using a large general-purpose model. OCI Generative AI supports fine-tuning of smaller models like Cohere Command Light on dedicated AI clusters, enabling domain-specific optimization without the expense of running a large model for every inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a large base model (e.g., Cohere Command) on a serverless endpoint
Why it's wrong here
Large model incurs higher cost per token.
- ✗
Use a large base model on a dedicated AI cluster
Why it's wrong here
Large model drives up cluster cost.
- ✗
Use a third-party LLM
Why it's wrong here
Third-party may introduce compliance and cost issues.
- ✓
Fine-tune a smaller base model on a dedicated AI cluster
Why this is correct
Smaller fine-tuned model reduces cost while meeting specialization needs.
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