Google Cloud Professional Machine Learning Engineer — Question 330

You are an AI architect at a popular photo sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?

Answer options

• A. Train an image clustering model by using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.
• B. Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.
• C. Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy into a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.
• D. Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.

Correct answer: C

Explanation

Option C is the best choice as it utilizes AutoML to create an image classification model that can directly integrate with the upload process to prevent explicit content. Option A focuses on clustering, which is less effective for classification tasks, while Option B relies on batch processing which is not timely for live uploads. Option D requires manual intervention by the content moderation team, which defeats the purpose of automating the process.