Q: 8
A company wants to build, tram, and deploy machine learning (ML) models.
Which AWS service can the company use to meet this requirement?
Options
Discussion
D imo
D or C? SageMaker lets you actually build and deploy any ML model, not just for one use case. Forecast (C) is only for time series prediction, so it's a bit of a trap if you read too quickly. Pretty sure D fits the broader need. Disagree?
D SageMaker is the go-to for building, training, and deploying ML models on AWS. The others are more for specific tasks, not end-to-end ML. Saw a similar question pop up during my practice. Anyone see differently?
Yeah, D fits because SageMaker handles general build, training, and deployment steps for ML models. The others are more focused on specific ML tasks, not the whole pipeline. Pretty sure about D here but open if someone thinks otherwise.
Had something like this in a mock before, is anyone sure C covers the full build/train/deploy flow or only specific use cases?
Why not D? The others only do one ML use case but SageMaker covers full build/train/deploy.
I'd probably go with C here since Amazon Forecast is specifically made for ML forecasting tasks. It's focused but still involves building and deploying models for time series data. Not 100 percent sure since D covers broader ML workflows, so happy to hear other takes.
D makes sense here. SageMaker is designed for building, training, and deploying all types of ML models, not just specific use cases. Saw a similar question in some practice exams where they always pick D for full ML workflows. Pretty sure that's what AWS wants here but let me know if anyone has seen it picked differently.
C/D? If they want custom ML workflows, D SageMaker is the way. C is just for forecasting so feels too narrow. Official guide points directly to SageMaker for end-to-end ML tasks. But let me know if you see it differently.
A is wrong, D. SageMaker is the one for end-to-end ML building, training, and deploying. Others are just for specific ML solutions.
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