Google Exam Professional-Cloud-Architect Topic 1 Question 74 Discussion

Actual exam question for Google's Google Cloud Architect Professional exam

Question #: 74
Topic #: 1

[All Google Cloud Architect Professional 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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Eladia

10 days 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

11 days 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

12 days 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

13 days 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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