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Answer: Translate the normalization algorithm into SQL for use with BigQuery.
The correct answer is B: Translate the normalization algorithm into SQL for use with BigQuery. By translating the Z-score normalization algorithm into SQL, you can leverage BigQuery's powerful processing capabilities to perform the normalization directly within BigQuery. This approach minimizes computation time and manual intervention because it eliminates the need to move data between different services. BigQuery can handle the computation internally, allowing for a more streamlined and efficient process. The other options are either adding unnecessary complexity (like using Kubernetes or Spark) or are not as efficient for this specific task.
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You are managing a demand forecasting pipeline in production. This pipeline leverages Dataflow to preprocess raw data before model training and prediction. The preprocessing step involves applying Z-score normalization to data stored in BigQuery, and the normalized data is then written back to BigQuery. New training data is added to the pipeline on a weekly basis. To enhance the efficiency of this process and reduce both computation time and manual intervention, what should you do?
A
Normalize the data using Google Kubernetes Engine.
B
Translate the normalization algorithm into SQL for use with BigQuery.
C
Use the normalizer_fn argument in TensorFlow's Feature Column API.
D
Normalize the data with Apache Spark using the Dataproc connector for BigQuery.
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