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 — Replace the four `log_metric` calls with a single `mlflow.log_metrics({"accuracy": acc, "precision": prec, "recall": rec, "f1": f1}, step=epoch)` call. A . `log_metrics` accepts a dictionary and sends all the named values for a given step in a single tracking-server call, which cuts four calls down to one per epoch while logging identical data. B . `log_param` stores fixed configuration values and is not intended for per-epoch numeric series; using it here also does not solve the batching goal, since it is still a per-call operation. C . Opening a nested run per epoch adds run-management overhead and does not batch metric writes into fewer calls; it would in fact multiply the number of tracking-server interactions. D . Uploading a JSON artifact each epoch still requires one call per epoch and adds file-storage overhead, and it loses the native metric charting that `log_metrics` provides in the UI.