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Google Professional Data Engineer Exam - Topic 1 Question 67 Discussion

Actual exam question for Google's Professional Data Engineer exam
Question #: 67
Topic #: 1
[All Professional Data Engineer Questions]

You are preparing an organization-wide dataset. You need to preprocess customer data stored in a restricted bucket in Cloud Storage. The data will be used to create consumer analyses. You need to follow data privacy requirements, including protecting certain sensitive data elements, while also retaining all of the data for potential future use cases. What should you do?

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Suggested Answer: C

by Denise at Mar 22, 2023, 05:20 PM

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Cathrine
5 months ago
Wait, can we really use CMEK like that? Sounds sketchy!
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Jesus
5 months ago
D sounds risky, sharing keys could lead to breaches.
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Wendell
6 months ago
C is interesting, but encrypting might complicate future analyses.
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Kina
6 months ago
I disagree, B is better since it removes sensitive fields entirely.
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Isadora
6 months ago
A seems like the best option for masking sensitive data.
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Hector
6 months ago
I recall that using customer-managed encryption keys is important for compliance, but I’m not clear on how that fits with the other options here.
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Titus
6 months ago
I’m a bit confused about whether encrypting data with Cloud KMS is necessary if we’re just masking sensitive data.
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Theodora
6 months ago
I think option A sounds familiar because it mentions using Dataflow and the DLP API together, which we practiced in a similar question.
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Devorah
6 months ago
I remember studying about the Cloud Data Loss Prevention API, but I'm not sure if it should be used for masking or removing data.
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Michel
6 months ago
I vaguely recall something about customer-managed encryption keys in our study materials. Option D seems to align with that, but I’m uncertain about using federated queries with it.
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Stefan
6 months ago
I feel like I saw a similar question about encryption in our practice exams. Option C mentions using Cloud KMS, which sounds familiar, but I'm not sure if it's the best approach here.
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Ceola
6 months ago
I'm not entirely sure, but I think option B could be a good fit too. It talks about detecting and removing sensitive fields, which seems relevant to data privacy.
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Sharika
6 months ago
I remember we discussed the importance of data masking in our last class. I think option A might be the right choice since it mentions masking sensitive data.
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Altha
6 months ago
Upgrading the system model seems like it would likely require a full system outage, so I'm going to rule that out. I'll focus on the first two options.
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Audry
6 months ago
The key here is understanding how explicit settings and ACT settings are prioritized. I believe option C is the false statement.
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Xochitl
6 months ago
I think Ethereum is a permissionless blockchain. We discussed that in class!
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Avery
11 months ago
Honestly, I'm just hoping the correct answer isn't 'All of the above'. That would be a real head-scratcher!
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Scarlet
10 months ago
C) Use Dataflow and Cloud KMS to encrypt sensitive fields and write the encrypted data in BigQuery. Share the encryption key by following the principle of least privilege.
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Reta
10 months ago
B) Use the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields from the data in Cloud Storage. Write the filtered data in BigQuery.
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Louvenia
10 months ago
A) Use Dataflow and the Cloud Data Loss Prevention API to mask sensitive data. Write the processed data in BigQuery.
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Dannie
12 months ago
Gotta love these data privacy questions! Option A sounds like a nice balance between masking sensitive info and keeping the full dataset. Not bad, not bad at all.
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Altha
10 months ago
Yeah, it's crucial to follow data privacy requirements while preprocessing customer data.
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Agustin
10 months ago
Using Dataflow and the Cloud Data Loss Prevention API seems like a solid approach.
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Ruthann
10 months ago
I agree, it's important to balance data privacy with retaining useful information.
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Alisha
10 months ago
Option A sounds like a good choice. It masks sensitive data while keeping the full dataset.
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Mammie
10 months ago
Yeah, it's crucial to follow data privacy requirements when handling customer data.
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Roy
10 months ago
Using Dataflow and the Cloud Data Loss Prevention API seems like a solid approach.
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Patria
10 months ago
I agree, it's important to balance data privacy with retaining useful information.
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Craig
10 months ago
Option A sounds like a good choice. It masks sensitive data while keeping the full dataset.
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Monroe
12 months ago
Hmm, Option B might be the easiest solution, but I'm worried about potentially losing valuable data in the process. Better to err on the side of caution.
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Ettie
11 months ago
User 2: I agree, it's better to be cautious and not risk losing any valuable information.
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Nichelle
11 months ago
User 1: I think Option B is the safest choice to protect sensitive data.
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Diane
1 year ago
I'm leaning towards Option D. Handling the encryption at the storage level and using federated queries in BigQuery could be more efficient and secure.
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Tawna
10 months ago
Definitely. This approach can help protect sensitive data while retaining it for future use cases.
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Quiana
11 months ago
It's important to follow the principle of least privilege when sharing encryption keys.
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Jina
11 months ago
I agree. Using federated queries in BigQuery can also help maintain data privacy.
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Leila
11 months ago
Option D sounds like a good choice. Encrypting the data at the storage level is a secure approach.
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Melvin
1 year ago
Option C seems like the way to go. Encrypting the sensitive data while retaining the full dataset for future use is a smart approach.
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Lisha
1 year ago
That's a good point, but I still think option A is more secure in terms of protecting sensitive data elements.
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Flo
1 year ago
I disagree, I believe option B is better as it uses the Cloud Data Loss Prevention API and Dataflow to detect and remove sensitive fields.
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Lisha
1 year ago
I think option A is the best choice because it uses Dataflow and the Cloud Data Loss Prevention API to mask sensitive data.
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