PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #57
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #57: Real Exam Question with Answer & Explanation
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Question
You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?
Options
- ANormalize the data using Google Kubernetes Engine.
- BTranslate the normalization algorithm into SQL for use with BigQuery.
- CUse the normalizer_fn argument in TensorFlow's Feature Column API.
- DNormalize the data with Apache Spark using the Dataproc connector for BigQuery.
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