AI Associate AI Fundamentals Practice Question
Which THREE of the following are best practices for training an Einstein Bot?
⚠ Common exam trap
Salesforce often tests the misconception that more training data is always better, but the trap here is that candidates may overlook the importance of diversity and negative examples, thinking that exact matches or a single intent simplify training, when in fact they cripple the bot's NLP accuracy.
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
✓
Test the bot with sample conversations before deployment
Testing an Einstein Bot with sample conversations before deployment allows you to validate the bot's intent recognition, dialog flow, and response accuracy in a controlled environment. This practice helps identify and fix issues with phrase matching, slot filling, and escalation paths, ensuring the bot performs reliably in production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train with only one intent to avoid confusion
Why it's wrong here
Bots need multiple intents to handle different customer requests.
- ✓
Test the bot with sample conversations before deployment
Why this is correct
Testing ensures the bot performs as expected in real scenarios.
- ✗
Use exact match phrases only
Why it's wrong here
Exact matches limit the bot; it should generalize from phrase variations.
- ✓
Use a large and diverse set of training phrases for each intent
Why this is correct
Diverse phrases improve the bot's ability to recognize variations.
- ✓
Include negative examples to improve accuracy
Why this is correct
Negative examples help the bot distinguish between similar phrases.
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