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Answer: Use AI Platform's batch prediction feature for automated processing, configuring it to automatically process new files added to Cloud Storage daily.
The correct answer is to utilize the batch prediction capability of AI Platform. This approach is cost-effective and scalable, automatically processing large datasets stored in Cloud Storage without manual intervention. It efficiently handles the daily volume of forms, ensuring data privacy and compliance by processing data in batches, and eliminates the need for manual data processing or real-time processing overhead.
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As the lead Machine Learning Engineer at your company, you're tasked with developing ML models to digitize scanned customer forms. You've created a TensorFlow model that converts these scanned images into text and stores them in Cloud Storage. The company processes thousands of forms daily, and the solution must be cost-effective, scalable, and require minimal human intervention. Additionally, the solution must ensure data privacy and compliance with industry standards. Which of the following approaches best meets these requirements? (Choose one correct option)
A
Set up a serving pipeline in Compute Engine for predictions, ensuring auto-scaling is enabled to handle the load.
B
Deploy the model on AI Platform and create a version for online inference, using Cloud Scheduler to trigger predictions at scheduled intervals.
C
Leverage Cloud Functions to predict each time new data is added to Cloud Storage, ensuring immediate processing of each form.
D
Use AI Platform's batch prediction feature for automated processing, configuring it to automatically process new files added to Cloud Storage daily.
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