Google Professional Cloud Architect Exam - Topic 1 Question 74 Discussion

Actual exam question for Google's Professional Cloud Architect exam

Question #: 74
Topic #: 1

[All Professional Cloud Architect Questions]

Your company has a Google Cloud project that uses BlgQuery for data warehousing There are some tables that contain personally identifiable information (PI!) Only the compliance team may access the PH. The other information in the tables must be available to the data science team. You want to minimize cost and the time it takes to assign appropriate access to the tables What should you do?

A1 From the dataset where you have the source data, create views of tables that you want to share, excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team
3 Assign access controls to the dataset that contains the view

B1 From the dataset where you have the source data, create materialized views of tables that you want to share excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team 3. Assign access controls to the dataset that contains the view.

C1 Create a dataset for the data science team
2 Create views of tables that you want to share excluding Pll
3 Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset

D1. Create a dataset for the data science team.
2. Create materialized views of tables that you want to share, excluding Pll
3. Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset

Show Suggested Answer

Suggested Answer: C

This option can help minimize cost and time by using views and authorized datasets. Views are virtual tables defined by a SQL query that can exclude PII columns from the source tables. Views do not incur storage costs and do not duplicate data. Authorized datasets are datasets that have access to another dataset's data without granting direct access to individual users or groups. By creating a dataset for the data science team and creating views of tables that exclude PII, you can share only the relevant information with the team. By assigning an appropriate project-level IAM role to the members of the data science team, you can grant them access to the BigQuery service and resources. By assigning access controls to the dataset that contains the view, you can grant them access to query the views. By authorizing the view to access the source dataset, you can enable the view to read data from the source tables without exposing PII. The other options are not optimal for this scenario, because they either use materialized views instead of views, which incur storage costs and duplicate data (B, D), or do not create a separate dataset for the data science team, which makes it harder to manage access controls (A). Reference:

https://cloud.google.com/bigquery/docs/views

https://cloud.google.com/bigquery/docs/authorized-datasets

by Adell at Dec 22, 2023, 12:11 AM

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Zena

4 months ago

Wait, can views really protect PII effectively? Not so sure about that.

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Jody

4 months ago

D sounds like overkill, just stick to views!

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Nathalie

4 months ago

C has too many steps, why complicate it?

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Princess

4 months ago

I think B is better since materialized views can improve performance.

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Lili

4 months ago

Option A seems the simplest, just views without PII.

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Latrice

5 months ago

I’m a bit confused about whether to use views or materialized views. I thought views were sufficient, but I see both options in the answers.

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Brittni

5 months ago

I’m leaning towards option C because it includes creating a dataset specifically for the data science team, which seems like a good way to manage access.

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Chauncey

5 months ago

I think option A sounds familiar from our practice questions, but I feel like creating a separate dataset might be a better approach for organization.

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An

5 months ago

I remember we discussed the importance of using views to restrict access to PII, but I'm not sure if materialized views are necessary here.

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Crissy

5 months ago

Okay, I've got a plan. I'll create a separate dataset for the data science team, build views that exclude the PII data, and then carefully assign the right access controls. That way we can minimize costs and ensure the right people have the right level of access.

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Paul

5 months ago

Hmm, I'm a bit confused about the difference between views and materialized views. I'll need to review the details on those options to make sure I understand the tradeoffs before answering.

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Wendell

5 months ago

This seems like a straightforward question about managing access to sensitive data in BigQuery. I think the key is to create views that exclude the PII data and then assign the appropriate IAM roles to the data science team.

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Marshall

5 months ago

This seems pretty straightforward. I'd go with option A - create views, assign IAM roles, and set the access controls. Materialized views might be overkill for this use case, and creating a separate dataset adds unnecessary complexity.

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Timothy

5 months ago

This question seems straightforward, but I want to make sure I understand the Universal Design for Learning principles before answering.

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Tamra

5 months ago

Hmm, I'm a bit confused about the relationship between the OCC ARM regulation and this new regulation. I'll need to re-read the passage carefully.

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Talia

6 months ago

I remember learning about CSQs in class, but I can't quite recall all the specific fields. I'll have to make an educated guess.

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Eladia

2 years ago

Ah, but then you'd have to maintain those materialized views, which could get tricky. I'd rather go with the simpler option of regular views and just make sure the access controls are set up properly. Honestly, any of these options would probably work, as long as you're careful about the IAM roles and access permissions.

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Dana

2 years ago

Hmm, I'm not sure about that. Doesn't option B also cover the key steps, and it's a bit more concise? Creating materialized views instead of regular views might be a bit more efficient, especially if the data science team needs to query these tables frequently.

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Tammi

2 years ago

I agree, option C looks the most comprehensive. Creating a separate dataset for the data science team is a good way to isolate the PII-containing tables and maintain tight access controls. The only thing I'm not sure about is the need to authorize the view to access the source dataset - I wonder if that's strictly necessary if the view is already excluding the PII.

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Ernie

2 years ago

This question seems straightforward, but the details around access controls and authorizing views can get tricky. I'm leaning towards option C since it covers all the necessary steps, including creating a separate dataset for the data science team and authorizing the views to access the source data.

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