AI Associate AI Capabilities in CRM Practice Question
Which TWO are best practices when implementing Einstein Recommendation Builder?
⚠ Common exam trap
Salesforce often tests the misconception that 'more data is always better' or that 'simplifying the model by disabling feedback improves performance,' when in reality, data quality and feedback signals are critical for accurate recommendations.
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
✓
Use a dataset that reflects recent customer interactions
Einstein Recommendation Builder relies on recent customer interaction data to generate accurate and relevant product or content recommendations. Using stale or outdated data can lead to irrelevant suggestions, as the model learns from historical patterns that may no longer reflect current customer preferences or behaviors.
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 dataset that reflects recent customer interactions
Why this is correct
Recent data provides timely recommendations.
- ✗
Disable negative feedback to simplify the model
Why it's wrong here
Negative feedback helps improve recommendations.
- ✗
Include all available data, even if it is old or unrelated
Why it's wrong here
Irrelevant data can degrade recommendations.
- ✗
Use a single dataset that contains the entire history of the org
Why it's wrong here
Too much historical data may not be beneficial; focus on relevant time frames.
- ✓
Regularly review and test the recommendation model
Why this is correct
Monitoring ensures model accuracy.
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