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Microsoft Exam DP-100 Topic 8 Question 103 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 103
Topic #: 8
[All DP-100 Questions]

You manage an Azure Machine Learning workspace.

You experiment with an MLflow model that trains interactively by using a notebook in the workspace. You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow. Which syntax should you use?

Show Suggested Answer Hide Answer
Suggested Answer: B

Contribute your Thoughts:

Evan
2 months ago
Remember, always double-check your spelling and syntax. Wouldn't want to lose points over a silly mistake!
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Kirk
2 months ago
C looks promising, but it's for logging a list of artifacts, not a single dictionary. I'm sticking with A.
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Gracia
2 months ago
B is close, but it's for logging metrics, not artifacts. I'll go with A.
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Nieves
10 days ago
Great, let's go with A to log the artifacts in Azure Machine Learning.
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Aja
13 days ago
Yes, A is the right choice for logging dictionary type artifacts.
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Hui
29 days ago
I think A is the correct syntax to log dictionary type artifacts in Azure Machine Learning using MLflow.
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Paris
2 months ago
Haha, D is clearly a typo. Maybe the exam writer was hungry and accidentally typed 'my diet' instead of 'my_dict'.
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Tonette
8 days ago
C) mlflow.log_artifacts(my_dict)
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Stevie
12 days ago
B) mlflow.log_metric(\'my_metric\', my_dict)
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Garry
1 months ago
A) mlflow.log_artifact(my_dict)
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Alline
2 months ago
I'm not sure, but I think C) mlflow.log_artifacts(my_dict) could also work for logging dictionary type artifacts
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Pearlene
2 months ago
I think A is the correct answer. We need to use `mlflow.log_artifact()` to log dictionary type artifacts.
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Cecil
1 months ago
User2: Yes, we need to use mlflow.log_artifact() to log dictionary type artifacts.
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Antione
1 months ago
User1: I think A is the correct answer.
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Dexter
2 months ago
I agree with Kris, A) mlflow.log_artifact(my_dict) makes sense for logging dictionary type artifacts
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Kris
3 months ago
I think the correct syntax is A) mlflow.log_artifact(my_dict)
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