1Z0-1127-25 Using OCI Generative AI Service Practice Question
A financial services company deployed a fine-tuned model using OCI Generative AI Service to generate investment advice based on quarterly reports. The model was trained on 10,000 labeled examples and achieved high accuracy in testing. However, after three months in production, the model's outputs have become inconsistent and sometimes recommend investments based on outdated market conditions. The team has received multiple complaints from users about inaccurate advice. The model is deployed on a dedicated AI cluster with auto-scaling disabled. The OCI audit logs show no configuration changes. The team suspects data drift and wants to mitigate it without incurring high costs. They have a pipeline that can collect new labeled data monthly, but it takes two weeks to process. What should the team do?
⚠ Common exam trap
Oracle often tests the misconception that hyperparameter tuning (like temperature) or infrastructure scaling can fix data drift, when in reality only retraining with fresh, representative data addresses the root cause.
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
✓
Set up a monthly retraining schedule using the new labeled data as soon as it is available, and use a champion/challenger deployment to validate the new model before full rollout.
It directly addresses data drift by establishing a regular retraining cycle with the new labeled data, which is the standard mitigation strategy for model degradation over time. The champion/challenger deployment pattern allows the team to validate the updated model's performance against the current production model before full rollout, ensuring no regression in accuracy. This approach balances cost efficiency (monthly retraining) with the operational constraint of a two-week data processing pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set up a monthly retraining schedule using the new labeled data as soon as it is available, and use a champion/challenger deployment to validate the new model before full rollout.
Why this is correct
Monthly retraining with fresh data mitigates drift, and champion/challenger ensures safe deployment.
- ✗
Decrease the temperature parameter to 0.1 to make outputs more deterministic.
Why it's wrong here
Temperature controls randomness, not the knowledge cutoff; it cannot fix outdated information.
- ✗
Revert to the base model (Cohere Command) and use few-shot prompting with recent reports.
Why it's wrong here
Reverting loses fine-tuning benefits, and few-shot prompting may not handle the complexity of investment advice.
- ✗
Enable auto-scaling on the dedicated AI cluster to handle increased load.
Why it's wrong here
Auto-scaling addresses performance under load, not model accuracy or drift.
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Senior Network & Security Engineer · founder of Courseiva
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