Question 4 of 280
Single answerYou want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data
transformations at scale, but your pipelines are taking over 12 hours to run. To speed up 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 the speed and processing
requirements?
AUse Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.
BConvert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to
write the data into BigQuery.
CIngest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform the data,
and then use federated queries from BigQuery for machine learning.
✓DIngest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL
queries to transform the data, and then write the transformations to a new table.
✓
Correct Answer: D
Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.
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Explanation
The correct answer is highlighted above. Review the wording carefully, then use the next question to continue building your understanding of Google certification topics.