AI Associate AI Fundamentals Practice Question
A company uses Einstein Bots to handle customer service inquiries. The bot often fails to understand complex requests, leading to escalations. Which improvement strategy is most effective?
⚠ Common exam trap
Salesforce often tests the misconception that increasing confidence thresholds or simplifying logic improves accuracy, when in fact these actions reduce the bot's ability to handle complex inputs, leading to more escalations.
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
✓
Train the bot with additional intents and example phrases for complex scenarios.
Training the bot with additional intents and example phrases directly addresses the root cause of the bot's failure: insufficient training data for complex scenarios. By expanding the training corpus, the natural language understanding (NLU) model can better recognize and classify nuanced user inputs, reducing misclassifications and unnecessary escalations.
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 the bot with additional intents and example phrases for complex scenarios.
Why this is correct
More training data improves NLU accuracy.
- ✗
Route all complex requests directly to human agents without bot interaction.
Why it's wrong here
This undermines the bot's purpose.
- ✗
Increase the confidence threshold for intent matching to avoid misclassification.
Why it's wrong here
Higher threshold may cause the bot to not understand more queries.
- ✗
Reduce the number of dialogue options to simplify the bot's logic.
Why it's wrong here
This limits the bot's capability.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.