Exam DP-100 topic 3 question 166 discussion

The correct answers are: A. MLflowClient.log_batch B. mlflow.log_metrics

No log_metrics, just log_metric

Performance considerations: If you need to log multiple metrics (or multiple values for the same metric), avoid making calls to mlflow.log_metric in loops. Better performance can be achieved by logging a batch of metrics. Use the method mlflow.log_metrics which accepts a dictionary with all the metrics you want to log at once or use MLflowClient.log_batch which accepts multiple type of elements for logging. See Log curves or list of values for an example.

If you need to log multiple metrics (or multiple values for the same metric), avoid making calls to mlflow.log_metric in loops. Better performance can be achieved by logging a batch of metrics. Use the method mlflow.log_metrics which accepts a dictionary with all the metrics you want to log at once or use MLflowClient.log_batch which accepts multiple type of elements for logging. See Log curves or list of values for an example. answer A,B

A & B are correct

A & B https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml-api-2&tabs=interactive

Correct answer! :) Performance considerations: If you need to log multiple metrics (or multiple values for the same metric) avoid making calls to mlflow.log_metric in loops. Better performance can be achieved by logging batch of metrics. Use the method mlflow.log_metrics which accepts a dictionary with all the metrics you want to log at once or use MLflowClient.log_batch which accepts multiple type of elements for logging. See here: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml-api-2&tabs=interactive SaM

there is no "log_metrics" but "log_metric"

AB is correct Performance considerations: If you need to log multiple metrics (or multiple values for the same metric) avoid making calls to mlflow.log_metric in loops. Better performance can be achieved by logging batch of metrics. Use the method mlflow.log_metrics which accepts a dictionary with all the metrics you want to log at once or use MLflowClient.log_batch which accepts multiple type of elements for logging. See Logging curves or list of values for an example. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml-api-2&tabs=interactive

A,C is right

There is no method called mlflow.log_metrics. It's mlflow.log_metric

A. MLflowClient.log_batch B. mlflow.log_metrics