AI Associate Data for AI Practice Question
An admin is troubleshooting Einstein Sentiment. The model returns high confidence but wrong sentiment (e.g., positive reviews labeled negative). What is the most likely issue?
⚠ Common exam trap
Salesforce often tests the concept that high confidence does not imply high accuracy; candidates mistakenly assume retraining or data volume issues are the root cause, rather than recognizing that garbage-in (incorrect labels) leads to garbage-out (confident wrong predictions).
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 training data has incorrect labels for sentiment.
If the training data contains incorrect labels for sentiment, the model learns from erroneous ground truth, leading to high confidence in wrong predictions. In Einstein Sentiment, the model's accuracy depends directly on the quality and correctness of the labeled training data; mislabeled examples cause the classifier to associate features with the wrong sentiment class, resulting in confident but incorrect outputs.
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 model was not retrained after the last data load.
Why it's wrong here
Retraining might be needed but wrong sentiment despite high confidence suggests labeling issues.
- ✗
The training data contains predominantly neutral examples.
Why it's wrong here
Imbalanced data can cause low confidence, not high confidence wrong predictions.
- ✓
The training data has incorrect labels for sentiment.
Why this is correct
Garbage in, garbage out: mislabeled training data leads to confident but incorrect classifications.
- ✗
The field mapping for the sentiment field is incorrect.
Why it's wrong here
Mapping issue would likely cause errors, not confident wrong predictions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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