C_AIG_2412 · Question #20
What must be defined in an executable to train a machine learning model using SAP AI Core? Note: There are 2 correct answers to this question.
The correct answer is A. Pipeline containers to be used B. Infrastructure resources such as CPUs or GPUs. When defining an executable in SAP AI Core to train an ML model, you must specify pipeline containers (A) - the Docker images that run each step of the training pipeline - and infrastructure resources (B) such as CPUs or GPUs, which tell AI Core how to provision the compute…
Question
What must be defined in an executable to train a machine learning model using SAP AI Core? Note:
There are 2 correct answers to this question.
Options
- APipeline containers to be used
- BInfrastructure resources such as CPUs or GPUs
- CUser scripts to manually execute pipeline steps
- DDeployment templates for SAP AI Launchpad
How the community answered
(21 responses)- A81% (17)
- C14% (3)
- D5% (1)
Explanation
When defining an executable in SAP AI Core to train an ML model, you must specify pipeline containers (A) - the Docker images that run each step of the training pipeline - and infrastructure resources (B) such as CPUs or GPUs, which tell AI Core how to provision the compute environment for the workflow.
Why C is wrong: SAP AI Core automates pipeline execution through Argo Workflows; users don't manually execute steps. The executable defines what runs, not manual triggers.
Why D is wrong: Deployment templates for SAP AI Launchpad relate to serving/inference configurations, not training executables. They are a separate concern used after a model is trained.
Memory tip: Think of an executable like a job posting - you must specify who does the work (containers = A) and what tools/equipment they need (CPU/GPU resources = B). Everything else (manual steps, deployment configs) happens outside the job definition itself.
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