1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company wants to use OCI Generative AI to summarize customer support tickets. They need to ensure that the model does not output any sensitive information. Which technique should they implement?
⚠ Common exam trap
Oracle often tests the misconception that disabling training data or using a smaller model can prevent sensitive output, when in fact prompt engineering is the primary technique for controlling model behavior at inference time in OCI Generative AI.
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
✓
Prompt engineering to instruct the model to exclude sensitive information.
Prompt engineering is the correct technique because it allows the company to explicitly instruct the generative AI model to exclude sensitive information from its outputs. By crafting a system prompt or user prompt with specific directives (e.g., 'Do not include any personally identifiable information, account numbers, or confidential data in your summary'), the model's behavior is directly controlled at inference time. This is a lightweight, flexible approach that does not require changing the model architecture or endpoint configuration, and it is the most direct way to enforce output constraints in OCI Generative AI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Prompt engineering to instruct the model to exclude sensitive information.
Why this is correct
Carefully crafted prompts can guide the model to avoid leaking sensitive data.
- ✗
Use a smaller model that is less likely to memorize data.
Why it's wrong here
Smaller models can still memorize and output sensitive information.
- ✗
Enable content filtering on the endpoint.
Why it's wrong here
Content filtering blocks harmful content but may not fully prevent sensitive data leakage.
- ✗
Disable the use of training data in the endpoint configuration.
Why it's wrong here
The endpoint does not use training data by default; the issue is model output.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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