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Answer: Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
The correct answer is C. Rather than building a new model, it is better to use Google's provided APIs. The Cloud Video Intelligence API can be used to generate labels for the entities in the videos, which saves time and resources compared to building and training a model from scratch. Cloud Bigtable is chosen to store data because it supports very fast filtering and can handle several TB of data efficiently. Bigtable is designed for low latency and high throughput, making it a better option for this task than Cloud SQL, which supports less data and involves more complex operations like joins.
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You are developing an application on Google Cloud that leverages a recommendation engine to suggest new videos to customers based on their past viewing history. Your solution needs to generate labels for the entities within the videos that the customers have previously watched. Additionally, the design must be capable of delivering very fast filtering suggestions using data from other customers’ preferences, handling several terabytes of data efficiently. What should you do?
A
Build and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.
B
Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
C
Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
D
Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.