1Z0-1127-25 Fundamentals of Large Language Models Practice Question
An LLM-based application must comply with data privacy regulations by not memorizing personally identifiable information (PII). Which technique best reduces memorization of PII?
⚠ Common exam trap
Oracle often tests the misconception that inference-time parameters like temperature or model size affect training data memorization, when in fact memorization is a training-phase phenomenon that must be addressed during training itself.
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
✓
Train with differential privacy
Differential privacy (DP) is the correct technique because it directly limits the model's ability to memorize training data, including PII, by adding calibrated noise to the gradient updates during training. This ensures that the model's parameters do not encode specific individual records, providing a formal mathematical guarantee against memorization. Other options like model size, temperature, or training epochs do not address the root cause of memorization in the training process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger model with more parameters
Why it's wrong here
Larger models tend to memorize more.
- ✗
Decrease the temperature during inference
Why it's wrong here
Temperature affects output randomness, not memorization.
- ✓
Train with differential privacy
Why this is correct
Differential privacy bounds the influence of any single data point, reducing memorization.
- ✗
Increase the number of training epochs
Why it's wrong here
More epochs can lead to overfitting and memorization.
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