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Microsoft Exam DP-100 Topic 4 Question 114 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 114
Topic #: 4
[All DP-100 Questions]

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?

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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
2 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
1 months ago
D) Instantiate the MLCIient class.
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Zachary
1 months ago
C) Log into workspace!
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Johnson
1 months 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
3 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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Mozell
2 months ago
D) Instantiate the MLClient class.
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Asha
2 months ago
C) Log into workspace!
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Kirk
2 months ago
B) Instantiate the job class.
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Kina
2 months ago
A) Configure the tracking URI.
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Ryan
3 months ago
Option A is the way to go. Configuring the tracking URI will allow you to log metrics and artifacts to your Azure Machine Learning workspace.
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Lynelle
2 months ago
No, that's not the right step for logging metrics and artifacts.
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Remona
2 months ago
D) Instantiate the MLCIient class.
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Merilyn
2 months ago
Yes, logging into the workspace is important for tracking.
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Kallie
2 months ago
C) Log into workspace!
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Kenda
2 months ago
Great idea! That will allow you to log metrics and artifacts.
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Adelina
3 months ago
A) Configure the tracking URI.
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Rebbecca
3 months ago
I think we should configure the tracking URI to continue logging metrics and artifacts.
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