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Question 94 - Professional Cloud Security Engineer discussion

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You are responsible for protecting highly sensitive data in BigQuery. Your operations teams need access to this data, but given privacy regulations, you want to ensure that they cannot read the sensitive fields such as email addresses and first names. These specific sensitive fields should only be available on a need-to-know basis to the HR team. What should you do?

A.
Perform data masking with the DLP API and store that data in BigQuery for later use.
Answers
A.
Perform data masking with the DLP API and store that data in BigQuery for later use.
B.
Perform data redaction with the DLP API and store that data in BigQuery for later use.
Answers
B.
Perform data redaction with the DLP API and store that data in BigQuery for later use.
C.
Perform data inspection with the DLP API and store that data in BigQuery for later use.
Answers
C.
Perform data inspection with the DLP API and store that data in BigQuery for later use.
D.
Perform tokenization for Pseudonymization with the DLP API and store that data in BigQuery for later use.
Answers
D.
Perform tokenization for Pseudonymization with the DLP API and store that data in BigQuery for later use.
Suggested answer: D

Explanation:

Pseudonymization is a de-identification technique that replaces sensitive data values with cryptographically generated tokens. Pseudonymization is widely used in industries like finance and healthcare to help reduce the risk of data in use, narrow compliance scope, and minimize the exposure of sensitive data to systems while preserving data utility and accuracy.

https://cloud.google.com/dlp/docs/pseudonymization

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asked 18/09/2024
Junaid Sahebzada
33 questions
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