DEA-C01 · Question #307
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…
The correct answer is A. Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for. AWS Glue DataBrew provides a low-code way to implement automated data quality checks, including completeness rules to flag missing timestamp values and numeric range validation to flag temperature readings outside -50°C to 150°C.
Question
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 -50°C to 150°C. 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?
Options
- ACreate an AWS Glue DataBrew project to profile the sensor data Define completeness rules for
- BUse AWS Glue's Data Quality rules and machine learning (ML)-based anomaly detection to
- CCreate an AWS Lambda function to scan the sensor data files to validate temperature ranges.
- DCreate an AWS Glue DynamicFrame that uses a custom data quality operator to profile the
How the community answered
(27 responses)- A81% (22)
- B4% (1)
- C11% (3)
- D4% (1)
Explanation
AWS Glue DataBrew provides a low-code way to implement automated data quality checks, including completeness rules to flag missing timestamp values and numeric range validation to flag temperature readings outside -50°C to 150°C.
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