Google Professional Machine Learning Engineer Exam - Topic 10 Question 59 Discussion

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

Question #: 59
Topic #: 10

[All Professional Machine Learning Engineer Questions]

You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (Pll). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?

AUsing Dataflow, ingest the columns with sensitive data from BigQuery, and then randomize the values in each sensitive column.

BUse the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow with the DLP API to encrypt sensitive values with Format Preserving Encryption

CUse the Cloud Data Loss Prevention (DLP) API to scan for sensitive data, and use Dataflow to replace all sensitive data by using the encryption algorithm AES-256 with a salt.

DBefore training, use BigQuery to select only the columns that do not contain sensitive data Create an authorized view of the data so that sensitive values cannot be accessed by unauthorized individuals.

Show Suggested Answer

Suggested Answer: D

This approach would allow you to keep the critical columns of data while reducing the sensitivity of the dataset by removing the personally identifiable information (PII) before training the model. By creating an authorized view of the data, you can ensure that sensitive values cannot be accessed by unauthorized individuals.

by Leatha at Feb 04, 2023, 06:10 AM

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Heike

4 months ago

Definitely go with C, replacing sensitive data is a must!

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Jose

4 months ago

I think D is too limiting, we need all columns for training!

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Brynn

4 months ago

Wait, can we really just encrypt with AES-256? Seems risky!

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Audra

4 months ago

I disagree, randomizing values (A) might mess with data integrity.

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Jenise

4 months ago

Option B sounds solid, DLP is key for sensitive data!

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Ivette

5 months ago

I recall that simply removing sensitive columns might not be ideal since every column is critical. We need to ensure we still have usable data after processing.

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Cory

5 months ago

I'm leaning towards option B, but I’m a bit confused about the difference between Format Preserving Encryption and just regular encryption.

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Bea

5 months ago

I think using the DLP API is a good approach since it can help identify sensitive data. I practiced a similar question where we had to mask data before analysis.

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