AI-102 Practice Question: Implement natural language processing solutions
Your organization uses Azure AI Language for custom text classification. You have deployed a model to a dedicated endpoint. After updating the training data, you retrain and redeploy the model. Users report that the endpoint still returns predictions from the old model. What is the most likely cause?
⚠ Common exam trap
It's easy for candidates to assume retraining automatically updates the endpoint, but Azure AI Language requires an explicit deployment step to bind the new model to the endpoint.
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 new model is not yet deployed; you must deploy it to the endpoint
In Azure AI Language, retraining a custom text classification model does not automatically update the deployed endpoint. After training, you must explicitly deploy the new model to the endpoint using the 'Deploy model' action. Until that step is completed, the endpoint continues to serve predictions from the previously deployed model.
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 training data changes are not saved
Why it's wrong here
Training data is saved when you train.
- ✗
The project needs to be rebuilt from scratch
Why it's wrong here
Rebuilding from scratch is unnecessary.
- ✗
The endpoint has a caching issue
Why it's wrong here
There is no caching mechanism for model endpoints.
- ✓
The new model is not yet deployed; you must deploy it to the endpoint
Why this is correct
Redeploying the model to the same endpoint updates the active model.
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