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Answer: 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.
Option C is the correct answer because it addresses all key requirements: it uses supervised learning (AutoML image classification) with labeled data for high accuracy, provides real-time online inference to block uploads before they occur, and includes monitoring and retraining for continuous improvement. Option A is unsuitable as clustering is unsupervised and less accurate for explicit content detection. Option B uses batch inference, which doesn't prevent uploads in real-time. Option D relies on Cloud Vision API but processes images after upload, failing to block them preemptively. The community discussion (100% consensus for C) reinforces this, noting A's unsupervised approach, B's batch limitation, and D's post-upload flaw.
Author: LeetQuiz Editorial Team
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As an AI architect for a photo-sharing social media platform, how would you design and implement an AI service to automatically prevent users from uploading explicit images, replacing the current manual moderation process?
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.