AI Associate AI Fundamentals Practice Question
A Salesforce admin is troubleshooting Einstein Object Detection in a custom object. The model is predicting values, but the confidence score remains below 80% for most predictions. What should the admin investigate first?
⚠ Common exam trap
Salesforce often tests the misconception that low confidence is caused by configuration issues like permissions or API versions, when the root cause is almost always inadequate or imbalanced training 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
✓
The model training data size and distribution of values.
Low confidence scores in Einstein Object Detection typically indicate that the model has insufficient or imbalanced training data. Einstein AI requires a minimum number of labeled records (e.g., at least 100 per class) and a balanced distribution across predicted values to learn effectively. Without adequate data size and diversity, the model cannot generalize well, resulting in confidence scores below 80%.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The number of fields used as input features.
Why it's wrong here
Feature count is less likely the primary cause.
- ✗
The API version used in the integration.
Why it's wrong here
API version affects connectivity, not confidence.
- ✗
The permission set for the Einstein AI feature.
Why it's wrong here
Permissions affect access, not output.
- ✓
The model training data size and distribution of values.
Why this is correct
Insufficient or unrepresentative data lowers confidence.
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JA
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.