MLA-C01 · Question #233
An ML engineer needs to build a processing pipeline to identify and remove personally identifiable information (PII) from petabytes of unstructured data. The ML engineer will use the processed data to
The correct answer is A. Use the Apache Spark-based serverless engine from AWS Glue interactive sessions. Use the. AWS Glue interactive sessions provide a serverless Apache Spark environment that can process data at very large scale, and the AWS Glue Detect PII transform can detect, mask, or remove PII entities during ETL - making it suitable for building a petabyte-scale PII redaction pipeli
Question
An ML engineer needs to build a processing pipeline to identify and remove personally identifiable information (PII) from petabytes of unstructured data. The ML engineer will use the processed data to train ML models in Amazon SageMaker AI. Which solution will meet these requirements?
Options
- AUse the Apache Spark-based serverless engine from AWS Glue interactive sessions. Use the
- BUse AWS Glue Data Wrangler within Amazon SageMaker Canvas to detect and remove the PII.
- CUse the Amazon SageMaker Clarify API to detect and mask the PII data.
- DUse the DetectEntities API action in Amazon Comprehend to identify and remove the PII data.
How the community answered
(36 responses)- A72% (26)
- B8% (3)
- C6% (2)
- D14% (5)
Explanation
AWS Glue interactive sessions provide a serverless Apache Spark environment that can process data at very large scale, and the AWS Glue Detect PII transform can detect, mask, or remove PII entities during ETL - making it suitable for building a petabyte-scale PII redaction pipeline before training in SageMaker
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