AI Associate Data for AI Practice Question
Which TWO considerations are important when labeling data for a supervised learning model?
⚠ Common exam trap
Salesforce often tests the misconception that automated labeling is a complete substitute for human labeling, when in reality it requires careful validation and is typically used to augment, not replace, human effort.
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
✓
Maintaining consistent guidelines.
Maintaining consistent guidelines (A) is critical because supervised learning models learn patterns from labeled data; inconsistent labels introduce noise and confuse the model, degrading its accuracy. Labeler expertise (B) ensures that domain-specific nuances are correctly captured, which is especially important for tasks like medical imaging or legal document classification where errors have high cost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Maintaining consistent guidelines.
Why this is correct
Clear guidelines ensure labelers apply the same criteria, reducing variability.
- ✓
Labeler expertise.
Why this is correct
Expert labelers produce more accurate labels, especially for domain-specific tasks.
- ✗
Using automated labeling for all tasks.
Why it's wrong here
Automated labeling can introduce errors without human validation.
- ✗
Ignoring inter-labeler agreement.
Why it's wrong here
Low agreement indicates inconsistency; it should be monitored and addressed.
- ✗
Labeling only a small sample.
Why it's wrong here
A small sample may not represent the data distribution, leading to biased models.
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