Exam Professional Machine Learning Engineer topic 1 question 233 discussion

B is definitely right, no doubt

https://cloud.google.com/vertex-ai/docs/pipelines/dataflow-component#dataflowpythonjobop

My Answer: B Looking for the options, DataflowPythonJobOp can be used for parallelizing the preprocessing tasks, which is suitable for image resizing, converting to grayscale, and extracting features. dsl.ParallelFor could be useful for parallelizing tasks but might not be the most straightforward option for image preprocessing. Generally DataflowPythonJobOp is followed by WaitGcpResourcesOp. https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/fe7d3e4b8edc137d90ec061789b879b7cc8d3854/notebooks/community/ml_ops/stage3/get_started_with_dataflow_flex_template_component.ipynb

I go with B. Custom training is surely required. Discarding A because Spark is not mentioned anywhere in the problem description. C involves Kubeflow which seems a bit overkill imo. DataflowPythonJobOp operator lets you create a Vertex AI Pipelines component that prepares data -> seems like the appropriate course of action to me. https://cloud.google.com/vertex-ai/docs/pipelines/dataflow-component#dataflowpythonjobop

A. DataprocSparkBatchOp: While capable of data processing, it's less well-suited for image-specific tasks like resizing and grayscale conversion compared to DataflowPythonJobOp. C. dsl.ParallelFor, dsl.component: While offering flexibility, they require more manual orchestration and potentially less efficient for image preprocessing compared to DataflowPythonJobOp. D. ImageDatasetImportDataOp, AutoMLImageTrainingJobRunOp: These components are designed for AutoML Image training, not directly compatible with custom preprocessing and training tasks.