Microsoft DP-100 Exam - 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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Herminia

3 months ago

I disagree, it’s not about the MLCIient class here.

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Pamella

3 months ago

Wait, can you really log metrics without logging into the workspace first?

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Arlen

4 months ago

Definitely A, tracking URI is essential for logging.

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Bettina

4 months ago

I think it's B, instantiating the job class is key.

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Joni

4 months ago

Gotta configure the tracking URI for MLflow!

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Santos

4 months ago

Instantiating the MLCient class sounds familiar, but I can't recall if it directly relates to logging metrics in this context.

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Lettie

5 months ago

I feel like logging into the workspace is a basic step, but I’m not convinced it’s the main action needed for continuing to log metrics.

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Inocencia

5 months ago

I'm not entirely sure, but I think we also discussed instantiating the job class for running experiments. Could that be relevant here?

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Casey

5 months ago

I remember we talked about configuring the tracking URI in class; it seems like that might be the right step to log metrics in Azure.

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Dong

5 months ago

Logging into the workspace seems like an obvious step, but I'm not sure if that's the complete solution here. I'll need to think through the full process of setting up the MLflow integration with the Azure Machine Learning environment.

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Aliza

5 months ago

I feel pretty confident about this one. The key is to configure the tracking URI so that the MLflow logging from the local notebook can be captured on the Azure Machine Learning compute instance. I'll make sure to do that first before running the notebook.

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Raina

5 months ago

Okay, I'm a bit confused here. Do I need to instantiate the job class or the MLClient class? I'll need to double-check the documentation to make sure I understand the right approach.

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Vernell

5 months ago

Hmm, this looks like a tricky one. I think I'll need to carefully review the details about configuring the tracking URI for MLflow to work with the Azure Machine Learning compute instance.

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Brendan

5 months ago

Hmm, I'm not entirely sure about this one. Let me think it through - a programme resource could be an input, a process, or a grouping of projects. I'll have to weigh the options carefully.

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Rikki

1 year 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 year ago

D) Instantiate the MLCIient class.

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Marylyn

1 year ago

C) Log into workspace!

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Raul

1 year ago

A) Configure the tracking URI.

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Cristina

1 year 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

1 year 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 year ago

D) Instantiate the MLCIient class.

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Zachary

1 year ago

C) Log into workspace!

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Johnson

1 year ago

B) Instantiate the job class.

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Tammi

1 year ago

A) Configure the tracking URI.

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Melodie

1 year ago

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

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Fernanda

1 year ago

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

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Ettie

1 year 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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