When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) DataScience...

Detailed Answer in Step-by-Step Solution:

Objective: Define the role of score.py in OCI model artifacts.

Understand Artifacts: score.py is key for deployment runtime.

Evaluate Options:

A: Infra config—Handled by OCI settings, not score.py.

B: Inference logic—Correct; runs load_model(), predict().

C: Scaling—Set in deployment, not score.py.

D: Dependencies—In runtime.yaml, not score.py.

Reasoning: B aligns with score.py’s execution role.

Conclusion: B is correct.

OCI documentation states: “score.py (B) contains the inference logic, including functions to load the model and predict outputs, executed by the deployment endpoint.” A, C, and D are managed elsewhere—only B matches OCI’s design.

Oracle Cloud Infrastructure Data Science Documentation, "Model Artifact - score.py".