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Microsoft Exam AI-900 Topic 5 Question 61 Discussion

Actual exam question for Microsoft's AI-900 exam
Question #: 61
Topic #: 5
[All AI-900 Questions]

You plan to use Azure Machine Learning Studio and automated machine learning (automated ML) to build and train a model What should you create first?

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Suggested Answer: B

by Solange at Apr 12, 2024, 09:35 PM

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Crista
9 months ago
Having a registered dataset ensures data integrity and reusability.
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Reta
10 months ago
Why is that?
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Crista
10 months ago
I would actually create a registered dataset first.
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Tegan
10 months ago
It provides a centralized place to experiment and train models.
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Matthew
10 months ago
Why do you think that?
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Tegan
10 months ago
I think we should create a Machine Learning workspace first.
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Providencia
10 months ago
But without the workspace, where will you store and manage the project?
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Jerrod
11 months ago
That makes sense, having the dataset ready is crucial for training the model
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Graciela
11 months ago
Actually, I believe we should create a registered dataset first to have the data ready
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Providencia
11 months ago
I agree with creating the workspace is the foundation for the project
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Jerrod
11 months ago
I think we should create a Machine Learning workspace first
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Annabelle
1 years ago
You guys are right, the workspace is definitely the first thing you should create. Although, I have to admit, a Jupyter notebook could also be helpful for playing around with the automated ML features. But the workspace is still the essential starting point.
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Freeman
1 years ago
Hmm, I'm leaning towards C) a registered dataset. That's the data we're going to be working with, so it makes sense to get that sorted out first.
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Goldie
1 years ago
Yeah, I was thinking the same thing. Creating the workspace first just makes the most logical sense. Once you have that set up, then you can start working on the other pieces like the dataset and the designer pipeline.
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Cecil
1 years ago
You guys are all wrong. The correct answer is clearly B) a Machine Learning workspace. That's the hub where rything else happens.
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Claudio
1 years ago
I agree with Adell. The Machine Learning workspace is the foundation you need to build everything else on. Without that, you won't be able to access the automated ML tools or any of the other Azure ML services.
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Zita
10 months ago
I agree. It's the starting point for using Azure Machine Learning Studio.
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Cory
11 months ago
B) a Machine Learning workspace
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Emeline
1 years ago
Hold on, what about the Jupyter notebook? Isn't that where we do a lot of the exploratory data analysis and model experimentation?
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Adell
1 years ago
Hmm, this seems like a straightforward question. I think the answer is B) a Machine Learning workspace. That's the core component you need to set up before you can start using Azure Machine Learning Studio and automated ML.
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Hershel
1 years ago
Nah, I disagree. I think the Machine Learning designer pipeline is the way to go. That's where you actually build and train the model, isn't it?
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Leonida
1 years ago
I'm not so sure. I think you might need to have a registered dataset first, before you can do anything else. That's the foundation, isn't it?
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Jackie
11 months ago
That sounds right. Then you can move on to building a model with Azure Machine Learning Studio.
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Carmelina
12 months ago
So, the correct order would be: create a Machine Learning workspace, then a registered dataset?
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Renato
12 months ago
Yes, that's important too. It provides the environment for your work.
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Elizabeth
12 months ago
But don't we also need a Machine Learning workspace to work in Azure Machine Learning Studio?
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Amalia
12 months ago
I agree, that's the foundation for building and training a model.
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Adolph
12 months ago
I think you should create a registered dataset first.
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Dewitt
1 years ago
Hmm, this is a tricky one. I suppose the first step would be to create a Machine Learning workspace, right? That seems like the logical starting point to me.
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