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Question 232 - Professional Cloud Architect discussion

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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?

A.
1 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
Answers
A.
1 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
B.
1 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.
Answers
B.
1 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.
C.
1 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
Answers
C.
1 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
D.
1. 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
Answers
D.
1. 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
Suggested answer: C

Explanation:

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

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asked 18/09/2024
Maris Romans
31 questions
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