1Z0-1127-25 Using OCI Generative AI Service Practice Question
An enterprise deployed a custom fine-tuned model for generating financial reports. After the first month, the model's outputs began to include outdated information and occasional factual errors. The team suspects data drift. What is the best course of action?
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
✓
Retrain the model on the latest financial data and monitor for drift.
Data drift occurs when the input data distribution changes over time, causing the model's outputs to become outdated or inaccurate. Retraining the model on the latest financial data realigns it with the current data distribution, and ongoing monitoring helps detect future drift. Option A is incorrect because switching to a newer base model like Llama 3.1 without retraining does not incorporate the latest financial data and may not address domain-specific drift. Option B is incorrect because decreasing the temperature parameter reduces randomness in outputs but does not correct factual errors stemming from data drift. Option D is incorrect because increasing the max tokens value only allows longer responses and does not improve accuracy or address drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a newer base model like Llama 3.1 without retraining.
Why it's wrong here
A newer base model may still require fine-tuning on the specific domain data to be accurate.
- ✗
Decrease the temperature parameter to 0.1 to reduce model creativity.
Why it's wrong here
Temperature controls randomness, not factual accuracy; it won't fix outdated knowledge.
- ✓
Retrain the model on the latest financial data and monitor for drift.
Why this is correct
Retraining with current data mitigates data drift and improves output accuracy.
- ✗
Increase the max tokens value to allow longer responses.
Why it's wrong here
Max tokens only affects response length, not quality or timeliness.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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