DATABRICKS-MLA Question 34

Community Discussion

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• Chose A0

  - Correct answer : A — Type a filter expression such as `params.model_type = "random_forest" and metrics.f1_score > 0.85` into the run table's search box, which MLflow parses and applies directly against the logged parameters and metrics. A . The MLflow UI's search box accepts a SQL-like filter syntax referencing `params.<name>` and `metrics.<name>` combined with comparison operators and `and`, so this exact expression narrows the run table to runs matching both conditions at once. B . Column-header sorting only reorders visible rows by one field at a time; it provides no way to jointly satisfy a parameter equality condition and a metric threshold without manually scanning every row. C . The run table supports combined parameter and metric filtering natively through its search box, so exporting to a spreadsheet to combine two conditions is unnecessary and the Artifacts tab does not offer a full experiment CSV export in this way. D . The Parallel Coordinates Plot can visually emphasize a metric range along one axis, but it does not apply a `model_type` equality condition, so it would not isolate runs matching both criteria.