Question 230 of 280
Choose 2You are building a custom image classification model and plan to use Vertex AI Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline?
ADataprocSparkBatchOp and CustomTrainingJobOp
✓BDataflowPythonJobOp, WaitGcpResourcesOp, and CustomTrainingJobOp
Cdsl.ParallelFor, dsl.component, and CustomTrainingJobOp
DImageDatasetImportDataOp, dsl.component, and AutoMLImageTrainingJobRunOp
✓AImport the new model to the same Vertex AI Model Registry as a different version of the existing model. Deploy the new model to the same Vertex AI endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
BImport the new model to the same Vertex AI Model Registry as the existing model. Deploy the models to one Vertex AI endpoint. Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
CImport the new model to the same Vertex AI Model Registry as the existing model. Deploy each model to a separate Vertex AI endpoint.
DDeploy the new model to a separate Vertex AI endpoint. Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.
✓
Correct Answer: B, A
DataflowPythonJobOp, WaitGcpResourcesOp, and CustomTrainingJobOp
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Explanation
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