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You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups: * Data engineers, which require lull data lake access * Analytic users, which require access to curated data You need to assign access rights to these two groups. What should you do?
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Question 273 - Professional Data Engineer discussion

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Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

A.
1Create a project for storage of the data for your organization. 2 Create a central Cloud Storage bucket with three folders to store the files for each department. 3. Create a central BigQuery dataset with tables prefixed with the department name. 4 Give viewer rights for the storage project for the users of your departments.
Answers
A.
1Create a project for storage of the data for your organization. 2 Create a central Cloud Storage bucket with three folders to store the files for each department. 3. Create a central BigQuery dataset with tables prefixed with the department name. 4 Give viewer rights for the storage project for the users of your departments.
B.
1Create a project for storage of the data for each of your departments. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Create user groups for authorized readers for each bucket and dataset. 4 Enable the IT team to administer the user groups to add or remove users as the departments' request.
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B.
1Create a project for storage of the data for each of your departments. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Create user groups for authorized readers for each bucket and dataset. 4 Enable the IT team to administer the user groups to add or remove users as the departments' request.
C.
1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Publish the data that each department shared in Analytics Hub. 4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.
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C.
1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3. Publish the data that each department shared in Analytics Hub. 4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.
D.
1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones. 4 Enable each department to own and share the data of their data lakes.
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D.
1 Create multiple projects for storage of the data for each of your departments' applications. 2 Enable each department to create Cloud Storage buckets and BigQuery datasets. 3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones. 4 Enable each department to own and share the data of their data lakes.
Suggested answer: C

Explanation:

Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.

Architecture and functions in a data mesh - Google Cloud

Professional Data Engineer Certification Exam Guide | Learn - Google Cloud

Build a Data Mesh with Dataplex | Google Cloud Skills Boost

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asked 18/09/2024
alejandro capel
49 questions
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