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 changes are persisted automatically once the model trains successfully, so unsaved data cannot explain stale predictions. It is tempting because data-handling mistakes are a common cause of poor model behaviour, and would be correct if the retraining job had failed or the labelled examples had never been imported into the project.
- ✗
The project needs to be rebuilt from scratch
Why it's wrong here
Retraining creates a new model version; the deployed endpoint keeps serving the old version until you explicitly update the deployment to point at the new one. Rebuilding the project discards labelled data and configuration without changing which model version the endpoint serves, so it cannot resolve stale predictions.
- ✗
The endpoint has a caching issue
Why it's wrong here
Endpoint responses are served from the deployed model version, not from a cache, so caching does not explain stale predictions. It is tempting because caching is a familiar cause of outdated content elsewhere, and would be correct if the deployment had not been reassigned to the newly trained model version on the dedicated endpoint.
- ✓
The new model is not yet deployed; you must deploy it to the endpoint
Why this is correct
Retraining creates a new model version but does not automatically replace the one assigned to a deployed endpoint. The endpoint continues serving its previously assigned deployment until you explicitly deploy the updated model to it, which explains why users still receive old predictions.
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