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Google Professional-Data-Engineer Certification Exam is a highly recognized certification exam for professionals who want to validate their skills and expertise in data engineering. Professional-Data-Engineer exam is designed to test the candidates' knowledge of Google Cloud Platform (GCP) and its data engineering services. It requires a deep understanding of various data tooling, technologies, and services used to build and maintain data pipelines, data processing systems, and machine learning models.

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Google Professional-Data-Engineer Certification is a highly recognized credential in the field of data engineering. It is designed to demonstrate a candidate’s expertise in designing, building, and maintaining data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for experienced data professionals who are seeking to enhance their skills and knowledge in the areas of data engineering, cloud computing, and data analytics.

Google Certified Professional Data Engineer Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

A. Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project
B. Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level
C. Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range
D. Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

Answer: D

NEW QUESTION # 68
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?

A. Refresh your browser tab showing the visualizations.
B. Clear your browser history for the past hour then reload the tab showing the virtualizations.
C. Disable caching by editing the report settings.
D. Disable caching in BigQuery by editing table details.

Answer: C

Explanation:
Reference https://support.google.com/datastudio/answer/7020039?hl=en

NEW QUESTION # 69
You manage your company's BigQuery data warehouse. You need to implement a solution that enables the data science team to modify data for experiments without affecting the original tables, while minimizing additional storage costs. What should you do?

A. Create table clones of all the tables for the data science team to use.
B. Set up authorized views in a shared dataset that reference the original tables.
C. Create snapshots of all the tables and restore them for the data science team to use.
D. Create a separate dataset with full copies of all the tables for each member of the data science team.

Answer: A

Explanation:
BigQuery Table Clones are specifically designed for the use case where you need a writable copy of a table that is storage-efficient.
* Writable and Independent: Unlike views (which are read-only) or snapshots (which are read-only until restored), a table clone is a lightweight, writable copy. The data science team can perform DML operations (INSERT, UPDATE, DELETE) on the clone without those changes reflecting in the production base table.
* Minimal Storage Costs: Table clones use a copy-on-write mechanism. Initially, the clone consumes zero additional storage because it points to the same underlying physical data blocks as the base table.
You are only billed for the data that differs between the clone and the base table (i.e., new or modified rows in the clone).
* Correcting other options:
* A (Authorized Views): Views do not allow the data science team to modify or "experiment" with the data; they only allow querying of the existing production data.
* B (Snapshots): Snapshots are read-only. To modify them, they must be restored into a standard table, at which point you are charged for the full storage of that restored table, failing the
"minimize storage costs" requirement.
* D (Full Copies): This is the most expensive option as it duplicates all physical data, leading to significantly higher storage costs.
Reference: Google Cloud Documentation on BigQuery Table Clones:
"A table clone is a lightweight, writable copy of another table (called the base table). You are only charged for storage of data in the table clone that differs from the base table, so initially there is no storage cost for a table clone... Common use cases include: Creating sandboxes for users to generate their own analytics and data manipulations, without physically copying all of the production data." (Source: Introduction to table clones)

NEW QUESTION # 70
Which of these sources can you not load data into BigQuery from?

A. Google Cloud Storage
B. Google Drive
C. File upload
D. Google Cloud SQL

Answer: D

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference:
https://cloud.google.com/bigquery/loading-data

NEW QUESTION # 71
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. 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.
B. 1. Create 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.
C. 1. Create 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.
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.

Answer: D

NEW QUESTION # 72
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