Google Exam Professional-Machine-Learning-Engineer Topic 10 Question 31 Discussion

Actual exam question for Google's Google Professional Machine Learning Engineer exam

Question #: 31
Topic #: 10

[All Google Professional Machine Learning Engineer Questions]

You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

ANormalize the data for the training, and test datasets as two separate steps.

BSplit the training and test data based on time rather than a random split to avoid leakage

CAdd more data to your test set to ensure that you have a fair distribution and sample for testing

DApply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

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Suggested Answer: D

by Jess at May 05, 2022, 11:38 AM

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