Exam DP-100 topic 3 question 159 discussion

Actual exam question from Microsoft's DP-100

Question #: 159
Topic #: 3

HOTSPOT
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You monitor an Azure Machine Learning classification training experiment named train.classification on Azure Notebooks.

You must store a table named table as an artifact in Azure Machine Learning Studio during model training.

You need to collect and list the metrics by using MLflow.

How should you complete the code segment? To answer, select the appropriate option in the answer area.

NOTE: Each correct selection is worth one point.

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Suggested Answer:

by damaldon at July 5, 2023, 7:54 p.m.

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evangelist

4 months, 3 weeks ago

in MLflow, an artifact is any file that you want to store and associate with a particular experiment run.

upvoted 2 times

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damaldon

1 year, 3 months ago

Correct: # Add a metric for each column prefixed by metric name. Similar to log_row row1 = {"table.col1": 5, "table.col2": 10} # To be done for each row in the table mlflow.log_metrics(row1) # Using mlflow.log_artifact import json with open("table.json", 'w') as f: json.dump(table, f) mlflow.log_artifact("table.json") # Use MlFlow to retrieve the run that was just completed client = MlflowClient() finished_mlflow_run = MlflowClient().get_run("<RUN_ID>")

upvoted 2 times

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Exam DP-100 topic 3 question 159 discussion

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evangelist

damaldon

SY0-701