Google Cloud Professional Machine Learning Engineer — Question 198

You work as an analyst at a large banking firm. You are developing a robust scalable ML pipeline to tram 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?

Answer options

• A. Use Tabular Workflow for Wide & Deep through Vertex AI Pipelines to jointly train wide linear models and deep neural networks
• B. Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models
• C. Use Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models
• D. Use Cloud Composer to build the training pipelines for custom deep learning-based models

Correct answer: C

Explanation

The correct answer is C because TabNet is designed for interpretability and can effectively handle tabular data, making it suitable for your needs. Option A focuses on combining different model types but may not prioritize interpretability as much. Option B is more geared towards XGBoost models, which may not offer the same level of interpretability. Option D involves custom deep learning models that may not align with your interpretability focus.