Exam AI-102 topic 3 question 6 discussion

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

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

Based on best practices for the Custom Vision service, the correct sequence of three actions would be: Upload all the images. Get suggested tags. Review the suggestions and confirm the tags. This sequence minimizes retraining time because: First, you need to get all 1,000 images into the Custom Vision portal Then, instead of manually tagging all images (which would be time-consuming), you use the auto-tagging/suggestion feature Finally, you review and confirm these suggested tags to ensure accuracy before retraining This approach leverages Custom Vision's automated tagging capabilities to significantly reduce the manual effort required, while still maintaining quality through human review of the suggestions.

1) Upload all the images is CORRECT. The first step is to upload all 1,000 images to the Custom Vision portal without sorting them into categories. This minimizes manual effort and prepares the images for automated processing. 2) Get suggested tags is CORRECT. After the images are uploaded, the system can automatically generate tag suggestions based on existing data from the model. This saves time by reducing the need to manually assign tags. 3) Review the suggestions and confirm the tags is CORRECT. Once the system provides tag suggestions, you need to review and confirm them to ensure accuracy. This step ensures the images are correctly labeled, which is essential for retraining the model effectively.

1) Smart Labeler (Suggested Tags): The Smart Labeler functionality uses the latest trained iteration of the model to predict labels for new images. Therefore, even if the images are new, as long as they share similarities with what the model has already seen, the suggested tags feature can save time and effort. Official Documentation: The Azure documentation clearly states that the Smart Labeler can automatically suggest tags for uploaded images, provided they are similar in context to previously trained data. 2) Uploading All Images: Bulk uploading images is the most time-efficient method. There is no need to manually categorize or upload by folder. The official Azure documentation supports SuperPetey's reasoning

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

Can't be correct. You wanna tell me people should manually tag 1000 images? And how do you categorize them in folders when they have no association? Seems dumb It has to be 1. Upload all the Images 2. Get suggested tags 3. Review the suggested Tags and confirm

Group Upload category Tag

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.

Final Answer - Upload all - Get suggested tags - Review and confirm tags

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.

Well, it's a bit confusing. In both cases (ET answers and SuperPetey suggestion) - we will have to walk through the pictures manually if there is no info about them. IF they are stored in class folders - the ET answer is less time consuming, if not - it's not possible to tell if separating them manually or manual check of suggested tags will take less time.

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

I agree with SuperPetey. The answer should be 1.) upload all the images 2.) Get suggested tags 3.) Review the suggestions and confirm the tags Reason being that using the tools(suggested tags) would still applied to the new 1000 images item, even if those 1000 images doesn't associate with the original data pool. So, that means tagging even 1 less images using the suggested tags would still be faster than manually tagging them. https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/suggested-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

Was on exam 27/11/2021