Q: 1
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in
Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive dat
a. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?
Options
Discussion
I don't think it's C. D.
C imo. Macie is literally made for S3 sensitive data discovery, and Lambda is just event-driven so you skip managing any servers. D looks tempting for flexibility but EC2 adds way more ops overhead.
C
Probably C. Macie is purpose-built for discovering sensitive data in S3 with almost no setup, plus Lambda is serverless so you don't handle infra. D trips people up because Comprehend does PII but isn't as integrated for S3 and EC2 adds extra ops work. Unless I'm missing something, C matches "least overhead" best.
C tbh. Macie is built to scan S3 for sensitive info so it does the detection part for you, then Lambda takes care of removal without servers to manage. Minimal overhead compared to spinning up EC2 or custom jobs. Anyone disagree?
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