Microsoft AI-102 topic 3 question 6

The given answer is incorrect. The question emphasizes two things - 1) the model has already been trained 2) the solution should be expedient. The given answer will be very slow to manually tag 1,000 images. instead:

1.) upload all the images
2.) Get suggested tags
3.) Review the suggestions and confirm the tags

reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/suggested-tags

When you tag images for a Custom Vision model, the service uses the latest trained iteration of the model to predict the labels of untagged images
we need latest trained to predict the labels, but this isn NOT HAVE ANY ASSOCIATED DATA

Answer is correct.

When uploading all images from a same folder, you can tag all of them with the same value at the same time.
Then you wont tag all 1000 images one by one, but only once by category (which is time saving as the question ask for).

Also, even if model is already trained, images are uploaded to workspace, and not to specific trained iteration.
You then cannot get tag suggestion when importing an image. There is none, that feature simply does not exist.

Try by yourself :
https://learn.microsoft.com/en-us/training/modules/classify-images/5-exercise-custom-vision

This question was asked in the actual exam on April 30, 2024 (+9:00, Japan). I think SuperPetey's answer is CORRECT, because I passed the AI-102 exam with a score of 917/1000. Thank you very much.

To minimize the time required for retraining the model, the correct three steps are:

Upload all images: First, you need to bulk upload the 1000 new images to the Custom Vision service. This is the foundational step for preparing the data.

Get suggested tags: Utilize Custom Vision's functionality to automatically suggest tags for the uploaded images. This can significantly reduce the workload of manual tagging.

Review and confirm suggested tags: Finally, manually review and confirm the tags suggested by the system to ensure their accuracy. Then, use these tagged images to retrain the model.

This process leverages the automation tools provided by Custom Vision to streamline and expedite the data preparation process, particularly effective when dealing with a large number of untagged images.

The answer is correct - there is no magic here. You can’t suggest any new tags based on the model you currently have. Read the limitations of the smart labeler carefully: When to use Smart Labeler
Keep the following limitations in mind:
You should only request suggested tags for images whose tags have already been trained on once. Don't get suggestions for a new tag that you're just beginning to train.

Answer given would be only option IF model had not already been trained with images, so...
I agree with SuperPetey et al...

Upload
Get suggested tags
Review and confirm tags

1.) Upload all the images
2.) Get suggested tags
3.) Review the suggestions and confirm the tags

If an image does not have any associated TAG, we can add a new one while reviewing

https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-improving-your-classifier

@superpetey, kindly read through the article in the link you shared, I just did and confirmed from it that the provided answer by the platform is correct.

"You should only request suggested tags for images whose content has already been trained once. Don't get suggestions for a new tag that you're just beginning to train." And the question says RETRAINING of an existing model to which we are adding new images. So the response is actually wrong and @superpetey is correct

The point of machine learning is that a model eventually LEARNS how to do things independently. Even though there is no associated data, there is previous learning done and existing labels can be used. I am not sure why we would need ML if we still have to do things manually all the time?