Which of the following can you do after training a machine learning model in SAP AI Core? There are 2 correct answers to this question.

Question

Options

• AAssess the resource allocation costs during training.
• BAnalyze the model's performance metrics.
• CReview the Kubernetes infrastructure scalability.
• DValidate the accuracy of the model against business requirements.

• A
  17% (9)
• B
  75% (39)
• C
  8% (4)

Explanation

After training a model in SAP AI Core, the post-training workflow focuses on evaluating the model itself - not the underlying infrastructure. Option B is correct because SAP AI Core provides built-in capabilities to inspect performance metrics (loss, accuracy, F1, etc.) logged during the training run, allowing data scientists to understand how well the model learned. Option D is correct because validating the trained model against actual business requirements (e.g., "does it meet our 95% accuracy threshold?") is a core step before promoting a model to production via SAP AI Core's model lifecycle management.

A is wrong because resource allocation costs are monitored during training (or via billing dashboards), not as a post-training action within the model evaluation workflow. C is wrong because SAP AI Core abstracts away the Kubernetes layer - end users don't interact with or review Kubernetes infrastructure; that's managed transparently by the platform.

Memory tip: Think "train → inspect the model, not the machine." After training, your focus shifts entirely to the model's quality (metrics = B, business fit = D). Infrastructure and costs are platform-level concerns, not model-level ones.

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