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UiPath-SAIAv1 Exam - Topic 20 Question 5 Discussion

Actual exam question for UiPath's UiPath-SAIAv1 exam
Question #: 5
Topic #: 20
[All UiPath-SAIAv1 Questions]

Which are all the options for managing ML Skills?

Show Suggested Answer Hide Answer
Suggested Answer: A

In UiPath AI Center, ML Skills can be managed in various ways, allowing users to customize and control how these skills are deployed and used. The management options include:

Creating a new ML skill.

Stopping a deployed skill.

Redeploying an ML skill.

Updating to a new package version.

Rolling back to a previous version if needed.

Modifying GPU usage.

Modifying the use of AI units.

Making the skill public or private.

Deleting an ML skill when no longer needed.

This provides flexibility for both managing the ML infrastructure and optimizing resources in real-time.

For more details, refer to:

UiPath AI Center Documentation: Managing ML Skills

ML Skill Management Options: Managing Machine Learning Skills in AI Center


by Deja at Jul 06, 2025, 07:25 PM

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Celeste
5 months ago
I’m pretty sure you can’t stop ML skills, though.
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Cherrie
5 months ago
Wait, can you really roll back to a previous version? That’s cool!
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Lanie
6 months ago
I think they can also be modified to use GPU, right?
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Ceola
6 months ago
Option A has the most features, seems like the best choice!
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Shawn
6 months ago
ML skills can definitely be created and deleted.
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Maia
6 months ago
I feel like option C is the right choice since it includes GPU modifications, but I’m not completely confident about the public/private aspect.
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Precious
7 months ago
I recall that we could modify the skills to use or not use GPU, but I can't remember if that was in every option.
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Kate
7 months ago
I think option A has the most details, but I feel like I might be mixing up some of the features with other practice questions we did.
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Shelba
7 months ago
I remember that we discussed the different management options for ML skills, but I'm not sure if GPU modifications were included in all of them.
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Justa
7 months ago
Ah, this is a good one. I remember learning about the various management options for ML skills in class. I think I've got a good handle on this.
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Daisy
7 months ago
Okay, I see the different choices here. I'll need to read through each one closely to make sure I understand the nuances between them.
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Isaac
7 months ago
Hmm, let me think through this carefully. I want to make sure I don't miss any of the key options for managing ML skills.
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Josefa
8 months ago
This looks like a straightforward question about managing ML skills. I'm pretty confident I can handle this one.
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Delfina
10 months ago
That makes sense. It's always good to have flexibility in managing ML Skills.
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Billy
10 months ago
I think having the option to modify the use of GPU and Al units can be useful for optimizing performance.
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Barrie
10 months ago
I'm just hoping the exam doesn't ask us to actually implement these ML skill management features. That sounds like a lot of work!
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Maia
8 months ago
I know, it does sound like a lot to remember for the exam.
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Val
8 months ago
A) ML skills can be created, stopped, redeployed, updated to a new package version, rolled back to a previous package version, modified to use or not use GPU. modified to use or not use Al units, made public or private, or deleted.
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Dominic
10 months ago
I'm not sure about the GPU and Al units part. Do we really need those options?
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Bernardine
10 months ago
I'm feeling confident about C being the correct answer. The ability to manage all those different aspects of an ML skill is crucial for keeping things running smoothly.
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Odette
9 months ago
User 2: Agreed, being able to stop, redeploy, update, and modify ML skills is essential for success.
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Dorthy
10 months ago
User 1: I think C is the correct answer too. It covers all the important options for managing ML skills.
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Delfina
10 months ago
Yes, I agree. Option A seems to cover all the possible actions we can take.
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Antonio
10 months ago
Haha, I bet the exam writers had a field day coming up with all these options. C looks like the most comprehensive answer to me.
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Joesph
10 months ago
Hmm, I was debating between B and C, but I think C is the right answer. The ability to modify the use of GPU and AI units is a key feature for managing ML skills.
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Caitlin
9 months ago
Yes, that flexibility is definitely important when working with machine learning skills.
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Barrett
10 months ago
I agree, being able to modify the use of GPU and AI units is crucial for managing ML skills.
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Billy
11 months ago
I think the options for managing ML Skills are quite extensive.
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Gregoria
11 months ago
Wow, this question covers a lot of ground when it comes to managing ML skills. I'm pretty sure the answer is C, but I'm a little unsure about the GPU and AI units part.
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Talia
10 months ago
I'm leaning towards A as well, it seems to cover all the necessary options for managing ML skills.
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Lon
10 months ago
I agree, A seems to be the most comprehensive choice.
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Georgeanna
10 months ago
I think the answer is A. It covers all the options for managing ML skills.
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