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Question 18/137
You trained a model on data that is stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training by using the latest data in the bucket. Data preprocessing is required prior to the retraining. You want to build a simple and efficient near real-time ML pipeline in Vertex AI that will perform the data preprocessing when new data arrives in the bucket. What should you do?
Correct Answer: B
The requirement specifies a near real-time trigger based on new data arriving in a bucket .
* Event-Driven Architecture: A Cloud Run function (formerly Cloud Functions) is the standard Google Cloud tool for reacting to " Object Finalize " events in Cloud Storage. This ensures that the moment a file is uploaded, the process begins.
* Orchestration: Initiating a Vertex AI Pipeline from that function is the best practice for ML workflows. It allows for a structured, reproducible sequence of preprocessing and training.
* Feature Management: Storing the resulting features in Vertex AI Feature Store ensures they are organized and ready for both training and low-latency serving.
* Why other options are incorrect:
* Options C and D: These rely on " Cloud Scheduler " or " cron jobs, " which are time-based (e.g., every hour). If data arrives between intervals, it sits idle, failing the " near real-time " requirement.
* Option A: Manually using the SDK lacks the automation and orchestration benefits of a managed pipeline and doesn ' t address the trigger mechanism.
* Event-Driven Architecture: A Cloud Run function (formerly Cloud Functions) is the standard Google Cloud tool for reacting to " Object Finalize " events in Cloud Storage. This ensures that the moment a file is uploaded, the process begins.
* Orchestration: Initiating a Vertex AI Pipeline from that function is the best practice for ML workflows. It allows for a structured, reproducible sequence of preprocessing and training.
* Feature Management: Storing the resulting features in Vertex AI Feature Store ensures they are organized and ready for both training and low-latency serving.
* Why other options are incorrect:
* Options C and D: These rely on " Cloud Scheduler " or " cron jobs, " which are time-based (e.g., every hour). If data arrives between intervals, it sits idle, failing the " near real-time " requirement.
* Option A: Manually using the SDK lacks the automation and orchestration benefits of a managed pipeline and doesn ' t address the trigger mechanism.
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