Which statement best describes the impact of data imbalance on model evaluation?

The model may appear to perform well on accuracy but perform poorly on minority-class recall.

The model's F1-score will always be low regardless of the class distribution.

Precision and recall are unaffected by class imbalance.

Accuracy remains a reliable metric because it reflects overall performance.

DatabricksCertified Machine Learning Associate

Domain 3Objective 2

Data Preparation and Imbalance MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 5)

Part of the Section 3: Model Development domain, which makes up ~23% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–14 in this domain), expect 2–4 from this objective — we provide 21 practice questions to prepare you well beyond it. (estimate)

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Questions 21–21

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Which statement best describes the impact of data imbalance on model evaluation?
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Data Preparation and Imbalance MACHINE-LEARNING-ASSOCIATE Practice Questions (Page 5)

Questions 21–21

Which statement best describes the impact of data imbalance on model evaluation?

Keep studying — Section 3: Model Development