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Google Cloud Certified Professional Data Engineer · Google Cloud
Select one answer.
Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.
I don't even know if MLLib has out-of-the-box Computer Vision models. Developing this in Dataproc would be a nightmare. Using the computer vision API on the other hand makes perfect sense. The fact that the filtering must happen very fast and that this is a customer facing application points to BigTable so that there is very little latency and high availability. BigTable is eventually consistent but that doesn't really matter for this application. CloudSQL will ensure strong consistency which we don't really need but it is slower and supports max 64 TB. The description mentions multiple TBs. Not really sure what several means here, but Cloud SQL doesn't have a high cap.
We need a model that extracts labels from videos, so Vision API could be used. Then we need a DB very fast and that can handle several TB of data, so BigTable is the best choice. Answer is C.
Option C is the correct choice because it utilizes the Cloud Video Intelligence API to generate labels for the entities in the videos, which would save time and resources compared to building and training a model from scratch. Additionally, by storing the data in Cloud Bigtable, it allows for fast and efficient filtering of the predicted labels based on the user's viewing history and preferences. This is a more efficient and cost-effective approach than storing the data in Cloud SQL and performing joins and filters.