Free Top Amazon/AWS DEA-C01 Actual Exam Questions - Question 15 Discussion
The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to
make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)
I think B is a solid pick for real-time recommendations since it directly invokes the SageMaker endpoint. For the future inventory predictions, A seems like the best choice because Redshift ML can build models right inside Redshift, so it handles ongoing predictions without needing to export data elsewhere. C is more about offline training, which isn’t as useful for real-time or seamless future predictions. So I’d go with A and B here.
D imo because SageMaker Autopilot is focused on building models, not dashboards, so that’s out. E is definitely off since Redshift isn’t meant for file storage or archiving reports. That leaves A and B as the only viable options—A for using Redshift ML to handle predictions within the cluster, and B for invoking the SageMaker real-time endpoint directly. Makes sense to combine them to cover both real-time and future inventory needs without unnecessary steps.
I agree that B is solid for real-time since it directly calls the SageMaker endpoint. For the other one, I’m thinking about option C. Even though it talks about scheduling exports for offline training, that could cover the future prediction part because it implies training models regularly on updated data. A sounds good too, but I’m not sure if Redshift ML is meant for real-time recommendations or mostly batch predictions. So maybe B and C make a stronger pair, with B handling real-time and C addressing future forecasting through periodic training. What do you think about excluding A because o
Option B makes sense for real-time calls; option C seems unrelated to real-time needs.
A is good for handling future inventory predictions directly in Redshift. E is off since Redshift isn’t meant for archiving files, so definitely not that. B also fits for real-time calls to SageMaker endpoints.
A/B? Using Redshift ML (A) covers building models directly within Redshift for future needs, while B lets you get real-time predictions by calling SageMaker endpoints. C and E don’t really fit the real-time or prediction parts.
Option B works because invoking SageMaker endpoints directly from Redshift supports real-time predictions. Option D doesn’t fit since Autopilot isn’t for creating dashboards, so it’s not useful here.
B and A sound like the best fits for real-time and future prediction.