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Answer: Store the data in a Cloud Storage bucket, and create a custom container with your training application. In your training application, read the data from Cloud Storage and train the model.
The correct answer is A because it focuses on minimizing startup time. By storing the data in a Cloud Storage bucket and creating a custom container with only the necessary dependencies, you keep the container image lean. This leads to faster download and startup times compared to options that include additional data or dependencies in the container, which would increase the image size and potentially slow down the startup process. This approach allows the container to be more reusable and efficient for small datasets.
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You are tasked with training an XGBoost model on a small dataset using Google's Vertex AI service. Your training code includes custom dependencies that are not part of the standard library. Given that you want to minimize the startup time of your training job, how should you set up your Vertex AI custom training job?
A
Store the data in a Cloud Storage bucket, and create a custom container with your training application. In your training application, read the data from Cloud Storage and train the model.
B
Use the XGBoost prebuilt custom container. Create a Python source distribution that includes the data and installs the dependencies at runtime. In your training application, load the data into a pandas DataFrame and train the model.
C
Create a custom container that includes the data. In your training application, load the data into a pandas DataFrame and train the model.
D
Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run your training application. Create a Python source distribution that installs the dependencies at runtime. In your training application, read the data from Cloud Storage and train the model.
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