Exam Professional Machine Learning Engineer topic 1 question 197 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 197
Topic #: 1

[All Professional Machine Learning Engineer Questions]

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?

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

Show Suggested Answer

Suggested Answer: C 🗳️

by pikachu007 at Jan. 13, 2024, 4:07 a.m.

Comments

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guilhermebutzke

10 months, 1 week ago

Selected Answer: C

My Answer: C Link: https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/overview

upvoted 3 times

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ddogg

10 months, 2 weeks ago

Selected Answer: C

https://www.sciencedirect.com/science/article/pii/S0957417423000441 • When compared to XGBoost & GLM, TabNet provides better or comparable performance. • Unlike other Deep Learning models, TabNet is highly interpretable.

upvoted 3 times

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sonicclasps

10 months, 3 weeks ago

Selected Answer: C

agree, C, as this is specifically one of Tabnet's strengths

upvoted 1 times

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winston9

11 months, 1 week ago

Selected Answer: C

according to the documentation: "TabNet uses sequential attention to choose which features to reason from at each decision step. This promotes interpretability and more efficient learning because the learning capacity is used for the most salient features."

upvoted 1 times

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pikachu007

11 months, 1 week ago

Selected Answer: C

TabNet models are inherently more interpretable than deep neural networks or XGBoost models due to their attention mechanism. This aligns with the primary focus on interpretability.

upvoted 1 times

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