Q: 1
You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to
train several regression and classification models. Your primary focus for the pipeline is model
interpretability. You want to productionize the pipeline as quickly as possible What should you do?
Options
Discussion
B tbh, GKE with XGBoost custom training sounds scalable and lets you fine-tune stuff, which I thought is good for productionizing quickly. Plus XGBoost gives some model interpretability (like feature importance). Not 100% sure though, D might be more purpose-built for orchestration. Anyone see issues with B?
Why not just use A if you want to skip custom orchestration steps? Cloud Composer feels heavier for pure speed.
Pretty sure it's D. Official exam guide and practice questions both recommend Cloud Composer for pipeline orchestration and model interpretability.
Its D, seen similar question in a mock and Cloud Composer fits this use case.
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