Microsoft Exam DP-100 Topic 4 Question 114 Discussion

Actual exam question for Microsoft's DP-100 exam

Question #: 114
Topic #: 4

You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.

You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.

You need to continue to log metrics and artifacts from your data science code.

What should you do?

AConfigure the tracking URI.

BInstantiate the job class.

CLog into workspace'!.

DInstantiate the MLCIient class.

Show Suggested Answer

Suggested Answer: A

by Avery at Sep 15, 2024, 05:09 PM

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Rikki

2 months ago

Option B is an interesting choice, but it doesn't seem to be the right answer here. Instantiating the job class is more for managing the execution of your training job, not for logging metrics and artifacts.

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Lore

1 months ago

D) Instantiate the MLCIient class.

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Marylyn

1 months ago

C) Log into workspace!

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Raul

1 months ago

A) Configure the tracking URI.

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Cristina

2 months ago

C is a bit suspicious. Logging into the workspace seems unnecessary when you already have an Azure Machine Learning workspace set up. Unless there's some kind of authentication issue, I don't think that's the right answer.

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Jodi

2 months ago

I'm torn between A and D. Both seem to be the correct approach, but I'm curious to know if there's a specific reason why one might be preferred over the other.

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Toshia

25 days ago

D) Instantiate the MLCIient class.

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Zachary

26 days ago

C) Log into workspace!

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Johnson

29 days ago

B) Instantiate the job class.

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Tammi

1 months ago

A) Configure the tracking URI.

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Melodie

2 months ago

I'm not sure. Maybe we should also consider instantiating the MLCIient class for this task.

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Fernanda

2 months ago

I agree with Rebbecca. Configuring the tracking URI seems like the right step to take.

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Ettie

3 months ago

D looks good to me. Instantiating the MLClient class will give you the necessary interface to interact with MLflow from your Azure Machine Learning environment.

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