AI Associate Data for AI Practice Question
A company is preparing data for Einstein Article Recommendation. Which data source is most appropriate for training the model?
⚠ Common exam trap
Salesforce often tests the misconception that static data like user profiles or org metadata can substitute for behavioral training data, but the model fundamentally requires historical interaction signals to learn relevance.
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
✓
Historical article view and click data.
Einstein Article Recommendation uses supervised machine learning to predict which articles users are likely to find relevant. The model must be trained on historical user engagement signals—specifically article view and click data—to learn patterns of relevance. Without this behavioral data, the model cannot establish a correlation between user actions and article content.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Historical article view and click data.
Why this is correct
This captures user preferences directly.
- ✗
Org metadata.
Why it's wrong here
Metadata describes structure, not user interactions.
- ✗
System debug logs.
Why it's wrong here
Debug logs are not relevant to user behavior.
- ✗
User profile data only.
Why it's wrong here
Profile data lacks behavioral signals.
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