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Google Cloud Certified Google Professional Data Engineer Exam

Google Professional Data Engineer Exam

Last Update Jun 15, 2025
Total Questions : 376

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Questions 2

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

Options:

A.  

Change the processing job to use Google Cloud Dataproc instead.

B.  

Manually start the Cloud Dataflow job each morning when you get into the office.

C.  

Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

D.  

Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Discussion 0
Questions 3

Which is not a valid reason for poor Cloud Bigtable performance?

Options:

A.  

The workload isn't appropriate for Cloud Bigtable.

B.  

The table's schema is not designed correctly.

C.  

The Cloud Bigtable cluster has too many nodes.

D.  

There are issues with the network connection.

Discussion 0
Questions 4

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Options:

A.  

Load the data every 30 minutes into a new partitioned table in BigQuery.

B.  

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

C.  

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

D.  

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Discussion 0
Questions 5

You need to compose visualization for operations teams with the following requirements:

    Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)

    The report must not be more than 3 hours delayed from live data.

    The actionable report should only show suboptimal links.

    Most suboptimal links should be sorted to the top.

    Suboptimal links can be grouped and filtered by regional geography.

    User response time to load the report must be <5 seconds.

You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

Options:

A.  

Look through the current data and compose a series of charts and tables, one for each possible

combination of criteria.

B.  

Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

C.  

Export the data to a spreadsheet, compose a series of charts and tables, one for each possible

combination of criteria, and spread them across multiple tabs.

D.  

Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

Discussion 0
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