AI Associate Data for AI Practice Question
Which THREE actions are recommended when preparing data for Einstein Next Best Action? (Choose 3)
⚠ Common exam trap
Salesforce often tests the misconception that more action types or recent data alone improve model performance, when in fact the key requirements are balanced positive/negative examples, sufficient historical depth, and regular retraining with fresh interaction data.
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
✓
Provide data on which actions were offered and whether they were accepted
Einstein Next Best Action (NBA) requires historical interaction data showing which actions were offered and whether they were accepted to train the predictive model. This feedback loop enables the AI to learn which actions are most effective for specific customer contexts, directly improving recommendation accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Provide data on which actions were offered and whether they were accepted
Why this is correct
This is essential for reinforcement learning.
- ✗
Include at least 10 different action types per strategy
Why it's wrong here
There is no fixed minimum; quality over quantity.
- ✓
Record rejections (actions not taken) as negative examples
Why this is correct
Negative feedback is crucial for learning what not to recommend.
- ✗
Use only historical data from the last 30 days
Why it's wrong here
More history is generally better; 30 days may not capture stable patterns.
- ✓
Retrain the model weekly with fresh interaction data
Why this is correct
Regular retraining keeps the model up-to-date.
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