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This society is ever – changing and the test content will change with the change of society. You don't have to worry that our GCP-DE training materials will be out of date. In order to keep up with the change direction of the exam, our question bank has been constantly updated. We have dedicated IT staff that checks for updates every day and sends them to you automatically once they occur. The update for our GCP-DE learning guide will be free for one year and half price concession will be offered one year later. In addition to the constantly update, we have been working hard to improve the quality of our GCP-DE preparation prep. I believe that with the help of our study materials, the exam is no longer an annoyance. Hope you can give not only our GCP-DE training materials but also yourself a chance.

Google Data Engineer Sample Questions:

1. You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A) Python using MapReduce
B) HiveQL using Hive
C) PigLatin using Pig
D) Java using MapReduce

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

A) The workload isn't appropriate for Cloud Bigtable.
B) There are issues with the network connection.
C) The table's schema is not designed correctly.
D) The Cloud Bigtable cluster has too many nodes.

3. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A stock symbol followed by a timestamp
B) A sequential numeric ID
C) A timestamp followed by a stock symbol
D) A non-sequential numeric ID

4. Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A) Trigger that is a combination of other triggers
B) Trigger based on element size in bytes
C) Trigger based on element count
D) Trigger based on time

5. You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A) Create a table on Cloud Spanner, and insert and delete rows with the job information
B) Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C) Create an API using App Engine to receive and send messages to the applications
D) Create a table on Cloud SQL, and insert and delete rows with the job information

Solutions: