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Q1 Single choice

A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50C to 150C. The data quality checks must also identify records that are missing timestamp values.

The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.

Which solution will meet these requirements?

A
Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for timestamps. Set up numeric range validation for temperature values.
B
Use AWS Glue's Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.
C
Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.
D
Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data.
Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.

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