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Answer: Pub/Sub for message queuing, Cloud Function for triggering ML model execution, Video Intelligence API for video analysis, and Cloud Logging for monitoring.
**Correct Answer: C. Pub/Sub, Cloud Function, Video Intelligence API, Cloud Logging** Here’s why: - **Pub/Sub**: Efficiently handles the publishing and subscribing of messages for real-time video content analysis. - **Cloud Function**: Provides a serverless execution environment to process video content upon message receipt, ensuring scalability and cost-effectiveness. - **Video Intelligence API**: Offers specialized pre-trained models for comprehensive video analysis, including object detection and content classification, which is crucial for identifying inappropriate content without the need for custom model training. - **Cloud Logging**: Essential for monitoring the system's performance and logging analysis results for compliance and debugging purposes. Other options are less optimal due to: - **Cloud Vision API**: Primarily designed for image analysis, not video, making it less suitable for this scenario. - **Cloud IoT**: Irrelevant for video content analysis as it's tailored for IoT device management. - **Dataflow**: More suited for batch processing rather than real-time video analysis. - **AutoML Video Intelligence**: While powerful for custom model training, it requires more resources and time compared to leveraging the Video Intelligence API's pre-trained models, making it less cost-effective and slower to deploy.
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As an ML engineer at a media company, you're tasked with developing a real-time ML model to analyze video content for object detection and inappropriate content notification. The solution must be cost-effective, scalable, and comply with data privacy regulations. Given these constraints, which combination of Google Cloud products would best suit this project? (Choose one correct option)
A
Pub/Sub for message queuing, Cloud Function for triggering ML model execution, and Cloud Vision API for image analysis, with Cloud Logging for monitoring.
B
Pub/Sub for message queuing, Cloud IoT for device management, Dataflow for batch processing, Cloud Vision API for image analysis, and Cloud Logging for monitoring.
C
Pub/Sub for message queuing, Cloud Function for triggering ML model execution, Video Intelligence API for video analysis, and Cloud Logging for monitoring.
D
Pub/Sub for message queuing, Cloud Function for triggering ML model execution, AutoML Video Intelligence for custom model training, and Cloud Logging for monitoring.