Practice Test 4 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
A company has cloud data flow pipeline currently running in production. They have few changes in the pipeline to fix current bugs. Also, some of the transformation names have been updated for code ethics and therefore current data flow job needs to be updated. How should the cloud data flow job update?
A. Stop the existing pipeline. Replace the pipeline code for a prior job with the new updated code. Start the jobs again after code replacement.
B. Create another job with the same job name as the prior job. The previous job will complete after processing your inflight data. Once the previous job is completed, the new job will come into effect.
C. Create another job with the same job name as the prior job. Also, pass the –update option. The previous job will complete after processing your inflight data. Once the previous job is completed, the new job will come into effect.
D. Create another job with the same job name as the prior job. Also, pass the –update option. User needs to provide transform mapping using –transformNameMapping option. The replacement job preserves any intermediate state data from the prior job, as well as any buffered data records or metadata currently “in-flight” from the prior job. “In-flight” data will then be processed by the transforms in your new pipeline.
The correct answer is option D
As per Google Cloud official documentation (https://cloud.google.com/dataflow/docs/guides/updating- a-pipeline)
When you update a job on the Cloud Dataflow service, you replace the existing job with a new job that runs your updated pipeline code. The Cloud Dataflow service retains the job name but runs the replacement job with an updated job.
The replacement job preserves any intermediate state data from the prior job, as well as any buffered data records or metadata currently “in-flight” from the prior job. For example, some records in your pipeline might be buffered while waiting for a window to resolve.
“In-flight” data will still be processed by the transforms in your new pipeline. However, additional transforms that you add in your replacement pipeline code may or may not take effect, depending on where the records are buffered.
Option A is incorrect. For updating an existing pipeline, a new pipeline needs to be created with the same job name.
Option B is incorrect. For updating an existing pipeline, a new pipeline needs to be created with the same job name, and also –update option should be passed. Also, as transform names are updated in the new pipeline, hence –transformNameMapping needs to be supplied while creation of this new job.
Option C in incorrect. For updating an existing pipeline, a new pipeline needs to be created with the same job name and also –update option should be passed. Also, as transform names are updated in the new pipeline, hence –transformNameMapping needs to be supplied while creation of this new job.
Option D is correct. For updating an existing pipeline, a new pipeline needs to be created with the same job name and also –update option should be passed. Also, as transform names are updated in the new pipeline, hence –transformNameMapping needs to be supplied while creation of this new job. This mapping will contain old transform jobs mapped to their new transform job. Eg. — transformNameMapping={“oldTransform1″:”newTransform1″,”oldTransform2″:”newTransform2”,…}
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