Training data contains personal information. What must you ensure?
Why C is the answer
Data privacy, and proper handling of sensitive fields
Training data with personal information needs privacy controls and careful handling of sensitive fields.
In detail
- Personal data calls for privacy and careful handling.
- Plan this before training starts.
Use case
An illustrative scenario, not a real incident.
A team removes names from training data, and limits access to what remains sensitive.
See it in different ways
mindmap
root((ML data))
Processing
Dataflow
Features
Feature Store
Prototyping
Workbench
Colab Enterprise
Tracking
Experiments
ML Metadata lineageflowchart LR S0["Find sensitive fields"] --> S1["Remove or mask"] --> S2["Limit access"]
Key point: Handle sensitive fields properly.
Why the other options are wrong
- A. A higher learning rate on the first run
- A learning rate has no bearing on privacy.
- B. A wider range of hyperparameters for tuning
- Tuning has no bearing on privacy.
- D. A larger number of training epochs
- Epochs have no bearing on privacy.