Exam Professional Machine Learning Engineer topic 1 question 168 discussion

leverage Vertex AI's AutoML capabilities to automatically build a high-quality image classification model

Managed dataset has a size limitation of 100GB . Question states " a large dataset " . Unmanged dataset has not size limitation . Assuming large here implies > 100GB , it should eliminate answer C

You're just trying to TRAIN A MODEL, not set up a whole pipeline. Answer is clearly C

B is right in my opinion, while both options C and B involve importing labeled images into Vertex AI, using AutoML for image classification might not be the most suitable choice. TFX is a more specialized tool that provides a robust pipeline framework specifically designed for image classification tasks, making it a better fit for this particular use case.

https://cloud.google.com/vertex-ai/docs/tutorials/image-classification-automl/dataset

My answer: B TensorFlow Extended (TFX) and Kubeflow provide capabilities for building machine learning pipelines that can handle data stored in Google Cloud Storage (GCS). However, when it comes to ease of use specifically for working with data in GCS, TFX may have a slight edge over Kubeflow for 1- Integration with GCS- TensorFlow: TFX is tightly integrated with TensorFlow that has built-in support for GCS and provides convenient APIs for reading data directly from GCS buckets 2 - Abstraction of Data Handling TFX provides higher-level abstractions and components specifically designed for common machine learning tasks, including data preprocessing, model training, and model evaluation

It's C

95th is the similar question. https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#sdk

Very vaguely put. I choose C over B just because it sounds like a simpler approach, but both should theoretically work.