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
I was thinking A could work since Lambda is serverless and SageMaker has solid integration for ML models, so less ops to manage overall. Not totally sure since Macie seems more tailored for S3, but SageMaker plus Lambda still feels pretty light-touch to me. Anyone else think A is a close second?
Definitely C. Macie is made for this kind of S3 sensitive data detection and Lambda cuts out all the server management. Lowest ops compared to running ECS or EC2 for sure. Pretty sure that's what AWS expects here, but open to counterpoints.
Any reason not to just let Macie auto-detect and plug Lambda for cleaning? Feels like C is the lowest ops combo since both are managed, so you don’t have to deal with container or EC2 maintenance. Might be missing something though.
C, not A. Macie is built for S3 and Lambda is serverless so less ops work.
C tbh
Would anyone here use Macie over Comprehend for S3 sensitive data detection if lowest ops overhead is the main focus? I keep seeing official docs and exam guides push Macie for S3, but Comprehend is often mentioned too. Just double checking in case I missed a scenario from labs or practice tests where Comprehend would make more sense.
D or C. Comprehend can do PII detection and EC2 lets you customize, so I was thinking D at first. But C uses Macie, which is also for S3 data. The question traps you by making D look flexible but might be more overhead. Not fully sure though.
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