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PDE Practice Question: After deploying a model to Vertex AI Endpoints,…
After deploying a model to Vertex AI Endpoints, the prediction responses include unexpected data. The model returns logits instead of probabilities. What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between model training configurations and serving/post-processing steps, and the trap here is that candidates assume the endpoint or deployment configuration controls output formatting, when in fact the model's exported graph or serving function determines whether logits or probabilities are returned.
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 output is not post-processed
The most likely cause is that the model output is not post-processed. In Vertex AI Endpoints, models often output raw logits (unnormalized scores) from the final layer, and a softmax or sigmoid activation must be applied as a post-processing step to convert these logits into probabilities. Without this post-processing, the endpoint returns the raw logits, which is why the prediction responses contain unexpected 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.
- ✗
The model was trained with different loss
Why it's wrong here
Training loss does not determine whether the model outputs logits or probabilities.
- ✗
The input data is scaled incorrectly
Why it's wrong here
Input scaling affects model accuracy but not the output format.
- ✗
The endpoint is not properly configured
Why it's wrong here
Endpoint configuration does not affect model output interpretation.
- ✓
The model output is not post-processed
Why this is correct
Missing softmax or similar transformation leads to raw logits being returned.
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