Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You have an existing data pipeline which uses Apache Spark to transform the data to be exported in CSV format, to be later loaded into MySQL database for further analysis. The CTO decides it’s time to migrate the pipeline to Google Cloud. As a data architect, you are tasked to design the new pipeline with minimal changes to the current pipeline architecture for a smoother migration.
Which of the following approaches are best suitable for your CTO’s requirement?
A. Use Dataproc for data transformation. Use Google Storage for storing data.
B. Use Dataflow for data transformation. Use BigQuery for storing data.
C. Use Dataproc for data transformation. Use BigQuery for storing data.
D. Use Dataflow for data transformation. Use Google Storage for storing data.
Answer: C.
For this scenario, it’s recommended to migrate the existing pipeline as-is. So, for transformation, Dataproc should be used to run Apache Spark jobs. Hence, answers B & D are incorrect.
For data warehousing, BigQuery is a good alternative to MySQL. Since BigQuery supports SQL- querying and schema is structured. BigQuery can be also used for analysis and reporting integrating it with Dataprep & Data Studio. So, answer C is correct.
Source(s):
Cloud Dataproc: https://cloud.google.com/dataproc/
Cloud Dataflow: https://cloud.google.com/dataflow/
Option A suggests use of google storage for storing data, the data in google storage will be stored as objects which can’t be analyzed directly. It’ll have to either imported to My SQL etc.
Now, MySQl is not given in either of options. Hence, we can’t assume on our own. CTO requirement is finally to analyze the data.
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