Google Cloud Professional Data Engineer — Question 82

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Answer options

• A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
• B. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
• C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
• D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Correct answer: B

Explanation

The correct answer is B because introducing a new MapReduce job specifically for sensor calibration ensures that this crucial step is performed on the raw data before any further processing occurs. Modifying the existing transformMapReduce jobs (option A) could disrupt the current workflow, while options C and D do not adequately integrate sensor calibration into the ETL process, leaving it to users or relying on predictions instead of systematic application.