1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
Which TWO actions should be taken to monitor model drift in a deployed generative AI model? (Select TWO)
⚠ Common exam trap
Oracle often tests the distinction between monitoring actions (detecting drift) and remediation actions (retraining, labeling, deploying versions), so candidates mistakenly select retraining or labeling as monitoring steps.
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
✓
Compare inference statistics over time
Comparing inference statistics over time (Option A) is correct because model drift in generative AI is detected by monitoring changes in output distributions, token probabilities, or response patterns relative to baseline metrics. This allows you to identify when the model's behavior deviates from expected performance due to shifts in input data or underlying patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Compare inference statistics over time
Why this is correct
Tracking statistics like output length or sentiment can indicate drift.
- ✗
Retrain the model weekly
Why it's wrong here
Retraining is a response to drift, not a monitoring action.
- ✗
Use OCI Data Labeling for new data
Why it's wrong here
Data labeling is for preparing training data, not monitoring.
- ✓
Set up alerts on accuracy metrics
Why this is correct
Alerts on metrics like accuracy or loss can signal drift.
- ✗
Deploy multiple model versions
Why it's wrong here
Versioning is for management, not monitoring drift.
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