Q: 4
[Data Engineering]
A medical imaging company wants to train a computer vision model to detect areas of concern on
patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each
patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A
machine learning engineer needs to build a labeling pipeline.
Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?
Options
Discussion
C . Private workforce in SageMaker Ground Truth covers "authorized users only" since you control access, and using the built-in bounding box task means no custom labeling app needed. That keeps effort low compared to building your own tools or using Mechanical Turk (which isn't private enough for this kind of medical data). Unless there's some hidden requirement I missed, C fits best here.
C . You get private access control with SageMaker Ground Truth, and the bounding box task is built-in so no custom tool needed. Seems quickest for sensitive healthcare data, though open to other takes if I missed something.
C tbh, had something like this in a mock exam and C was correct for private, low-effort setup.
I think this is same as a common exam questions, in practice, pretty sure the answer is C
C imo. Only private workforce in SageMaker Ground Truth lines up with "authorized users only" and least setup effort.
I remember a similar scenario from labs and C was always the go-to. Setting up a private workforce in SageMaker Ground Truth with the bounding box task saves tons of setup compared to custom solutions, and it handles access for authorized users. Pretty sure that's what AWS expects here, but happy to hear if anyone has a real-world counterexample?
D imo
I’d say C here. Using a private workforce in SageMaker Ground Truth matches the need for restricting access to just authorized users, and the built-in bounding box task saves a ton of setup. Rest of the options are more work or don't properly handle PHI. Anyone disagree?
B , Mechanical Turk plus built-in Ground Truth tasks is pretty fast to set up. Saw similar in some practice guides. Only thing is the private data, but for least effort B looks close. Anyone see a rule against it in official docs?
C
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