Practice Test 1 | Google Cloud Certified Professional Cloud Architect | Dumps | Mock Test
You are using DataFlow to ingest a large amount of data and later you send the data to Bigquery for Analysis, but you realize the data is dirty and cannot be cleaned using predefined models, what would be the best choice to be used to clean the data in the stream with serverless approach?
A. Fetch data from Bigquery and clean data from DataPrep and send it back to Bigquery.
B. Fetch data from Bigquery and create one more pipeline, clean data using DataFlow and send it back to Bigquery.
C. Fetch the data from Bigquery and clean data from DataProc and send it back to Bigquery.
D. Send the data to Data Storage and use BigTable to clean the data.
Correct Answer: Option B
Option B is CORRECT
Dataflow is a good choice if data has no implementation with Spark or Hadoop and does not run on clusters, instead, it is based on parallel data processing. As such data is split processed on multiple microprocessors to reduce processing time.
Option A is INCORRECT. Dataprep uses Dataflow/ BigQuery under the hood, enabling to process structured or unstructured datasets of any size with the ease of clicks.
Dataprep optimizes the job execution leveraging Trifacta’s in-memory processing engine for small datasets, BigQuery SQL pushdown (ELT) when the data is already in BigQuery/Dataflow by parallel data processing framework for large distributed datasets. Dataprep uses predefined models to clean the data, which is not valid in this scenario.
Option C is INCORRECT because Dataproc is designed to run on clusters which makes it compatible with Apache Hadoop, hive, and spark. It is significantly faster at creating clusters and can autoscale clusters without interruption of the running jobs.
Option D is INCORRECT because Cloud Bigtable is Google’s NoSQL Big Data database service, it is not used to clean data.
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