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Answer: Create a Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage.
Option C is the best approach in this situation. It involves creating a Dataflow workflow to clean the data by setting out-of-range values to an appropriate default and writing the updated records to a new dataset in Cloud Storage. This ensures that the original data is kept intact for compliance purposes. Option A would remove data which might be needed for compliance, Option B would incur additional storage costs without necessarily solving the issue, and Option D would overwrite the original data, thereby violating compliance needs.
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You have uploaded 5 years of application log data to Google Cloud Storage for further analysis and storage. Recently, a user reported that certain data points within these logs fall outside the expected operational ranges, suggesting the presence of errors in the dataset. Your task is to identify and correct these anomalies, ensure the integrity of future log updates, and maintain the original log data for compliance and auditing purposes. What steps should you take to achieve these objectives?
A
Import the data from Cloud Storage into BigQuery. Create a new BigQuery table, and skip the rows with errors.
B
Create a Compute Engine instance and create a new copy of the data in Cloud Storage. Skip the rows with errors.
C
Create a Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage.
D
Create a Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage.
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