What does SAP recommend you do before you start training a machine learning model in SAP AI Core? Note: There are 3 correct answers to this question.

Question

Options

• AConfigure the training pipeline using templates.
• BDefine the required infrastructure resources for training.
• CPerform manual data integration with SAP HANA.
• DConfigure the model deployment in SAP Al Launchpad.
• ERegister the input dataset in SAP AI Core.

• A
  70% (23)
• C
  9% (3)
• D
  21% (7)

Explanation

Before training a machine learning model in SAP AI Core, SAP recommends three preparatory steps: configuring training pipelines via templates (A), defining required infrastructure resources (B), and registering the input dataset (E). These three together establish the "what runs, how it runs, and what it runs on" - templates define the workflow logic, resource definitions specify compute needs (memory, CPU/GPU), and dataset registration makes training data discoverable to the pipeline.

Why the distractors are wrong:

• C (Manual data integration with SAP HANA) is incorrect because SAP AI Core uses registered datasets and object stores - manual HANA integration is not part of the standard pre-training workflow.
• D (Configure model deployment in SAP AI Launchpad) is a post-training step; you deploy a model only after it has been trained and an artifact exists - doing this before training makes no sense in the workflow sequence.

Memory tip: Think of the three correct answers as "Template → Resources → Data" - the three pillars you must have in place before any training job can run. If you can't run it, configure it, or feed it data, training can't start. Deployment (D) and manual DB integration (C) are either out-of-sequence or outside the standard AI Core workflow entirely.

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