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Microsoft AI-900 Exam - Topic 3 Question 54 Discussion

Actual exam question for Microsoft's AI-900 exam
Question #: 54
Topic #: 3
[All AI-900 Questions]

You have an Azure Machine Learning model that uses clinical data to predict whether a patient has a disease.

You clean and transform the clinical data.

You need to ensure that the accuracy of the model can be proven.

What should you do next?

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Suggested Answer: D

by Dierdre at Nov 26, 2023, 04:38 PM

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Meaghan
4 months ago
Not sure if training first is the best move here.
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Annett
4 months ago
Automated ML sounds like a good option too!
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Charlesetta
4 months ago
Wait, can you really validate just with the clinical data?
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Gilberto
5 months ago
I agree, splitting is crucial for validation.
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Tonette
5 months ago
You should definitely split the data first!
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Kanisha
5 months ago
I practiced a question similar to this, and I think we definitely need to train the model first. So, maybe option A? But I’m not completely confident.
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Chan
5 months ago
I feel like validating the model is crucial, but I can't remember if we should do that before or after training. Is option D the right choice?
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Mike
5 months ago
I'm not entirely sure, but I remember something about using automated ML to improve model accuracy. Maybe option C is the way to go?
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Estrella
5 months ago
I think we need to split the clinical data into two datasets first, right? That way we can have a training set and a validation set.
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Myong
5 months ago
Okay, I've got this. The key here is to validate the model's accuracy, so I'm going to go with option D. After training the model on the clinical data, I'll use the same data to validate its performance and ensure it's accurate.
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Glenn
5 months ago
I'm a bit confused here. Should I be using automated ML to train the model, or is that not the best approach? I'm not sure if that would be enough to "prove" the accuracy of the model.
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Rebeca
5 months ago
Hmm, this seems like a straightforward question. I think I'll go with option B - splitting the clinical data into two datasets. That way, I can use one set to train the model and the other to validate its accuracy.
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Annmarie
6 months ago
I'm feeling pretty confident about this one. I think option A is the way to go - just train the model using the clinical data. That should give me a good starting point, and then I can focus on validating the model's accuracy.
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Yaeko
6 months ago
Yep, that's a smart move. Gotta make sure we're getting the data structured in a way that supports the analysis requirements.
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Lemuel
6 months ago
This seems like a pretty standard integration task. As long as I can find the right documentation on setting up the CEF connector, I should be able to knock this out without too much trouble.
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Jerrod
6 months ago
This is a good test of my knowledge on quality management. I'll carefully consider the responsibilities of each role to determine the best answer.
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Carmelina
2 years ago
Hmm, I'm not sure automated ML is the way to go here. Seems like a bit of overkill when you just need to prove the accuracy. I'd lean towards option B or D.
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Rosio
2 years ago
Ooh, option C is interesting too - using automated ML could help optimize the model and make sure it's as accurate as possible. Though I guess you'd still need to validate it somehow.
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Billye
2 years ago
I agree, validating the model is crucial. I'm thinking option B, splitting the data into two datasets, might be the way to go. That way you can train on one and test on the other to see how it performs.
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Van
2 years ago
D) Validate the model by using the clinical data.
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Juliana
2 years ago
A) Train the model by using the clinical data.
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Sylvia
2 years ago
B) Split the clinical data into Two datasets.
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Ernie
2 years ago
This is a tricky question. I think the key is to ensure the accuracy of the model can be proven. That means we need to validate it somehow, not just train it on the data.
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