Free Google Professional Data Engineer Actual Exam Questions - Question 2 Discussion
conduct data transformations at scale, but your pipelines are taking over twelve hours to run. To
expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You
have already moved your raw data into Cloud Storage How should you build the pipeline on Google
Cloud while meeting speed and processing requirements?
D, new subscription avoids compatibility issues and keeps data safe.
A/C? Draining the old pipeline (A) ensures no data loss by finishing all work, but creating a new pipeline on the same subscription (C) also avoids losing messages since Pub/Sub keeps them until acknowledged.
Maybe D here. Creating a new subscription means the new pipeline starts fresh without messing with the old one's processing state, so you avoid conflicts or compatibility issues. Since Pub/Sub retains messages until acknowledged, the new subscription will get all new data without loss. Then you can cancel the old pipeline safely after switching over. It feels cleaner than trying to reuse the same subscription with incompatible code, which might cause processing glitches or duplicates.
D, so you isolate the new pipeline and avoid conflicts from incompatible code changes.
A/C? Draining the old pipeline (A) ensures no data loss, but creating a new pipeline on the same subscription (C) can also work since Pub/Sub retains unacked messages, so the new pipeline picks up where the old left off.
It’s B. Using the transform mapping JSON lets you update the pipeline without breaking compatibility, so you can avoid data loss without needing to drain or switch subscriptions.
D - Are we sure the new subscription won't miss messages during the switch?