Databricks Certified Machine Learning Professional Practice Mode

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose A0

  - Correct answer : A — `mlflow.log_artifact("scaler.pkl")` for the single file, and `mlflow.log_artifacts("evaluation_report")` for the folder, since the plural form uploads a directory's contents. A . `log_artifact` uploads one local file, while `log_artifacts` walks a local directory and uploads its full contents, so pairing them this way matches each call to its intended input type. B . This reverses the actual behavior: `log_artifacts` expects a directory path and `log_artifact` expects a single file path, regardless of whether that file has a recognizable extension. C . `log_artifact` does not accept a directory path; passing a folder to it raises an error, since only `log_artifacts` is designed to recurse through a directory's contents. D . `log_model` is for logging a trained model in a specific ML flavor format with a signature, not for arbitrary serialized files, and it does not accept a directory of mixed report files.