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Free Microsoft AI-900 Azure AI Fundamentals Actual Exam Questions - Question 9 Discussion

Question No. 9

For a machine learning progress, how should you split data for training and evaluation?

Select one option, then reveal solution.

John T.

2026-02-20

B/D? Splitting columns (D) would mix up features and labels, so that’s a no. B makes sense because it keeps the samples intact, which is crucial for training and evaluation.

Amir D.

2026-02-17

Probably B, since splitting rows keeps the feature-label pairs intact for both training and evaluation. Splitting columns (D) would separate features and mess up the data.

Andre U.

2026-01-29

It’s definitely D that’s off since splitting columns means mixing up features and labels, which makes no sense for training vs evaluation. Between B and the others, B is the only one that correctly talks about splitting rows randomly, which keeps the data structure intact. A and C mix up features and labels, which can’t work since you need both in training and evaluation. So yeah, B is the way to go for a proper train/eval split.

Andre U.

2026-01-19

B imo, you gotta split rows not columns.