PROFESSIONAL-DATA-ENGINEER Exam Questions & Answers

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

A. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

B. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

C. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub. processes the

data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time

aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization.

What should you do?

A. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore

B. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore

C. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.

D. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.

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 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.

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.