1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An architect needs to ensure that an LLM deployed in OCI does not reveal sensitive information in its outputs. Which technique should be used?
⚠ Common exam trap
Oracle often tests the distinction between input-side controls (like sanitization) and output-side controls (like filtering), and the trap here is that candidates confuse input sanitization with output filtering, assuming that cleaning the input is sufficient to prevent data leakage from the model's training or internal knowledge.
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
✓
Output filtering via custom inference wrapper
Output filtering via a custom inference wrapper allows the architect to inspect and sanitize the model's generated text before it reaches the user, preventing the leakage of sensitive information such as PII, credentials, or internal data. This technique operates at the application layer, intercepting the LLM's response and applying rules or regex patterns to redact or block prohibited content, which is essential for compliance and data security in production deployments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Limiting max tokens
Why it's wrong here
Max tokens only limits length, not content sensitivity.
- ✗
OCI Data Safe masking
Why it's wrong here
Data Safe is for database security, not model outputs.
- ✓
Output filtering via custom inference wrapper
Why this is correct
A custom wrapper can filter outputs to remove sensitive information.
- ✗
Input sanitization
Why it's wrong here
Input sanitization reduces risk but does not protect against model generating sensitive content.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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