1Z0-1127-25 Using OCI Generative AI Service Practice Question
A healthcare company is deploying OCI Generative AI Service for clinical decision support. They must ensure that model outputs are auditable, explainable, and free from patient data exposure. Which combination of OCI features should they use?
⚠ Common exam trap
Candidates often assume that simply de-identifying data (Option A) or using a private endpoint (Option D) is sufficient for auditability and explainability, overlooking the need for explicit monitoring and logging mechanisms to capture and review model behavior.
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
✓
Use OCI Data Masking to de-identify inputs, and enable model monitoring with explainability outputs via OCI Monitoring and OCI Logging.
OCI Data Masking can de-identify patient data in inputs before they reach the generative AI model, ensuring no protected health information (PHI) is exposed. Enabling model monitoring with explainability outputs via OCI Monitoring and OCI Logging provides an auditable trail of model decisions and explanations, meeting the requirements for auditability and explainability in clinical decision support.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Fine-tune a model on de-identified patient notes and use default inference settings.
Why it's wrong here
Fine-tuning may risk overfitting or data leakage; default settings don't provide explainability.
- ✗
Use Retrieval-Augmented Generation with an internet search index for up-to-date medical knowledge.
Why it's wrong here
Internet search may expose patient queries and is not compliant with health data regulations.
- ✓
Use OCI Data Masking to de-identify inputs, and enable model monitoring with explainability outputs via OCI Monitoring and OCI Logging.
Why this is correct
Data masking ensures compliance, and monitoring with logging provides auditability and explainability.
- ✗
Deploy the model in a private endpoint and disable all logging to prevent data leaks.
Why it's wrong here
Disabling logging removes audit trails, which is unacceptable for clinical decision support.
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