AI Associate AI Fundamentals Practice Question
A data scientist is evaluating Salesforce's Einstein features for predictive analytics. Which three statements accurately describe Einstein Discovery? (Select three answers.)
⚠ Common exam trap
Salesforce often tests the misconception that Einstein Discovery requires external data preparation or has a small data limit, when in fact it is designed for enterprise-scale data and includes integrated data wrangling.
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
✓
It can generate natural language explanations of model insights.
Einstein Discovery includes a natural language generation (NLG) engine that automatically produces plain-English explanations of model insights, such as key drivers and predictions. This allows non-technical users to understand the output without needing to interpret raw statistical data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
It can generate natural language explanations of model insights.
Why this is correct
Natural language explanations are a key feature.
- ✗
It is limited to 1000 rows of data per prediction.
Why it's wrong here
It can handle millions of rows.
- ✓
It can automatically build regression and classification models from your data.
Why this is correct
Einstein Discovery uses automated machine learning.
- ✓
It can be used to create stories that explain key drivers of a metric.
Why this is correct
Stories provide insights in a narrative format.
- ✗
It requires a separate data preparation tool before use.
Why it's wrong here
It includes data preparation capabilities.
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