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Google Professional-Data-Engineer Dumps Questions Answers

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Last Update: Dec 3, 2023
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Google Professional Data Engineer Exam Questions and Answers

Questions 1

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

  • The user profile: What the user likes and doesn’t like to eat
  • The user account information: Name, address, preferred meal times
  • The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?





Cloud SQL


Cloud Bigtable


Cloud Datastore

Questions 2

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?



Rewrite the job in Pig.


Rewrite the job in Apache Spark.


Increase the size of the Hadoop cluster.


Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

Questions 3

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?



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


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


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


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.

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