PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #35
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #35: Real Exam Question with Answer & Explanation
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Question
You 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?
Options
- AUse Data Fusion's GUI to build the transformation pipelines, and then write the data into
- BConvert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on
- CIngest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform
- DIngest your data into BigQuery using BigQuery Load, convert your PySpark commands into
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