Exam Professional Machine Learning Engineer topic 1 question 17 discussion

Should be D https://cloud.google.com/architecture/automating-classification-of-data-uploaded-to-cloud-storage#building_the_quarantine_and_classification_pipeline

D; others pose risks

why not b, it requires real-time right?

D) The best choice for this scenario would be D. Create three buckets of data: Quarantine, Sensitive, and Non-sensitive. Write all data to the Quarantine bucket. Periodically conduct a bulk scan of that bucket using the DLP API, and move the data to either the Sensitive or Non-Sensitive bucket.

D - Quarantine bucket is the google reccomended approach

Option B does not provide a clear separation between sensitive and non-sensitive data before it is written to BigQuery, which means that PII might be exposed during the process. But, in D offers a better level of security by writing all the data to a Quarantine bucket first. This way, the DLP API can scan and categorize the data into Sensitive or Non-sensitive buckets before it is further processed or stored. This ensures that PII is not accessible by unauthorized individuals, as the sensitive data is identified and separated from the non-sensitive data before any further actions are taken.

real-time prediction engine, that streams files to Google Cloud. PII is not accessible by unauthorized individuals. D

D should be the correct answer

Went with D

B is real time

It's D

A, D, C they do not apply to a realtime case, all three say that the scan is applied periodically Then it's B

I think that is the correct because of the "real time" application.

D is the right answer: you can temporarily store the sensitive data in a Quarantine bucket with restricted access, then move the data to the relative buckets once the PII have been protected.

Correct answer is "D"

Answer is D : Question says that there MAY be sensitive data, so not all data is sensitive. This is why we need 3 buckets : Quarantine as a landing bucket, sensitive for sensitive data after DLP scan, non-sensitive for non-sensitive after DLP scan. https://cloud.google.com/architecture/automating-classification-of-data-uploaded-to-cloud-storage

Reason being "Real Time' DLP scanning. Option A would scan all the data again and again. For others - Buckets etc is overkill and offline process.