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Microsoft AI-900 Exam - 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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Gracia
3 months ago
Really? I’m surprised the workspace is the first step.
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Leatha
4 months ago
A Jupyter notebook seems like a good start too, right?
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Joaquin
4 months ago
Wait, I thought you could start with a registered dataset?
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Nieves
4 months ago
Totally agree, the workspace is essential.
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Selene
4 months ago
You need to create a Machine Learning workspace first!
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Ernest
4 months ago
I thought we always started with a workspace, but I could be wrong. The Jupyter notebook option seems like it could be relevant too.
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Luis
5 months ago
I’m leaning towards the Machine Learning workspace too, but I feel like I might be mixing it up with something else we studied.
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Twana
5 months ago
I remember practicing a question like this, and I think a registered dataset might be the first step, but it could also be the workspace.
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Rashad
5 months ago
I think we need to create a Machine Learning workspace first, but I'm not entirely sure.
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Evangelina
5 months ago
Okay, let me think this through. I believe the correct answer is to create a Machine Learning workspace first. That's the foundational component that will allow us to access all the Azure ML tools and services we need to build and train the model.
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Hector
5 months ago
I'm a bit unsure about this one. Is it possible we need to create a registered dataset first, before we can start building the model? That seems like a logical first step.
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Jesusita
5 months ago
Hmm, this seems straightforward. I think the first step would be to create a Machine Learning workspace, since that's the core environment where we'll be working with Azure ML Studio and automated ML.
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Leeann
5 months ago
I'm a little confused on this one. Should we be creating a Jupyter notebook first, to set up our coding environment? Or is that not necessary if we're using the automated ML capabilities in Azure ML Studio?
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Mike
5 months ago
This looks like a tricky one. I'll need to think through the Intermediate Routing pattern and how it might work with different architectural patterns.
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Rikki
5 months ago
I'm pretty sure the answer is C. The 'wq!' command in Vim saves the current file and exits the editor.
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Winfred
5 months ago
Okay, let's think this through step-by-step. MapReduce is used to process data stored in HDFS, so that's option C. YARN is the resource manager, not a specific application, so that rules out option D. I'm leaning towards C as the best answer.
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Crista
2 years ago
Having a registered dataset ensures data integrity and reusability.
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Reta
2 years ago
Why is that?
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Crista
2 years ago
I would actually create a registered dataset first.
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Tegan
2 years ago
It provides a centralized place to experiment and train models.
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Matthew
2 years ago
Why do you think that?
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Tegan
2 years ago
I think we should create a Machine Learning workspace first.
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Providencia
2 years ago
But without the workspace, where will you store and manage the project?
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Jerrod
2 years ago
That makes sense, having the dataset ready is crucial for training the model
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Graciela
2 years ago
Actually, I believe we should create a registered dataset first to have the data ready
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Providencia
2 years ago
I agree with creating the workspace is the foundation for the project
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Jerrod
2 years ago
I think we should create a Machine Learning workspace first
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Annabelle
2 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
2 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
2 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
2 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
2 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
2 years ago
I agree. It's the starting point for using Azure Machine Learning Studio.
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Cory
2 years ago
B) a Machine Learning workspace
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Emeline
2 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
2 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
2 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
2 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
2 years ago
That sounds right. Then you can move on to building a model with Azure Machine Learning Studio.
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Carmelina
2 years ago
So, the correct order would be: create a Machine Learning workspace, then a registered dataset?
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Renato
2 years ago
Yes, that's important too. It provides the environment for your work.
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Elizabeth
2 years ago
But don't we also need a Machine Learning workspace to work in Azure Machine Learning Studio?
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Amalia
2 years ago
I agree, that's the foundation for building and training a model.
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Adolph
2 years ago
I think you should create a registered dataset first.
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Dewitt
2 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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