Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
A system with Apache Kafka installed on Compute Engine virtual machines on Google Cloud is used to stream data feeds for a social media platform. Data include posts, comments, clicks and other behavior events on the platform’s pages.
As the platform expands to reach more users by day, it is noticed by the engineering team that the flow of data feeds is slowing down and the time gap between last feed in data sink and current time expands. After some investigation, it is observed that the bottleneck is from Kafka cluster that is unable to stream feeds incoming in a timely fashion. How would you solve this?
A. Build a Dataflow pipeline to ingest the data feed.
B. Scale-out Kafka cluster by doubling the number of VMs used.
C. Use Pub/Sub to ingest and stream data feeds instead of using Apache Kafka.
D. Spin up a new Kafka cluster and distribute data feed evenly between the two clusters.
Correct Answer: C
Cloud Pub/Sub is a service to ingest event streams at any scale. It’s scalable and reliable for stream analytics and event-driven computing systems. So it’s the most reliable Google product for such a scenario.
Option A is incorrect: Dataflow cannot ingest event streams. It needs Pub/Sub service to do so.
Options B & D are incorrect: These are not scalable solutions and eventually, this issue will arise again. Pub/Sub is a fully managed service which is scalable without user action.
Source(s):
Google Pub/Sub:
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