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Question 80

You work for a retailer that sells clothes to customers around the world. You have been tasked with ensuring that ML models are built in a secure manner. Specifically, you need to protect sensitive customer data that might be used in the models. You have identified four fields containing sensitive data that are being used by your data science team: AGE, IS_EXISTING_CUSTOMER, LATITUDE_LONGITUDE, and SHIRT_SIZE. What should you do with the data before it is made available to the data science team for training purposes?

Options:

A.

Tokenize all of the fields using hashed dummy values to replace the real values.

B.

Use principal component analysis (PCA) to reduce the four sensitive fields to one PCA vector.

C.

Coarsen the data by putting AGE into quantiles and rounding LATITUDE_LONGTTUDE into single precision. The other two fields are already as coarse as possible.

D.

Remove all sensitive data fields, and ask the data science team to build their models using non-sensitive data.

Question 81

You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?

Options:

A.

AutoML Natural Language

B.

Cloud Natural Language API

C.

AI Hub pre-made Jupyter Notebooks

D.

AI Platform Training built-in algorithms

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 1, 2024
Questions: 270
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