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UiPath Exam UiPath-SAIv1 Topic 2 Question 25 Discussion

Actual exam question for UiPath's UiPath-SAIv1 exam
Question #: 25
Topic #: 2
[All UiPath-SAIv1 Questions]

Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?

Show Suggested Answer Hide Answer
Suggested Answer: A

Contribute your Thoughts:

German
1 months ago
I'm with Daron on this one. Option D sounds like the kind of advice you'd get from someone who's never actually used an ML Extractor before. Definitely not the way to go.
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Mireya
11 days ago
I agree, option B seems to be the most logical choice when selecting a UiPath ML Extractor.
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Jade
13 days ago
I also believe that option B is the best approach. It's important to choose a trained ML Extractor.
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Lore
14 days ago
I think option B makes more sense. Considering document types and data quality is crucial.
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Youlanda
21 days ago
I agree with you, Daron's suggestion doesn't seem practical.
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Daron
1 months ago
Wow, Option D really takes the cake! Bigger models performing better? That's like saying the more ingredients you throw in a cake, the tastier it'll be. Nonsense!
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Renato
6 days ago
Marla: Exactly, that's what really makes a difference in performance.
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Marla
11 days ago
User 2: Definitely, it's more about the quality and relevance of the data used for training.
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Reid
16 days ago
User 1: I agree, bigger doesn't always mean better.
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My
2 months ago
I'm going to have to go with Option B as well. Bigger isn't always better when it comes to ML models. It's about finding the right tool for the job, not the flashiest one.
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Rochell
19 days ago
Absolutely, focusing on document types and data quality is key. Option B is the most practical approach when choosing a UiPath ML Extractor.
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Viola
20 days ago
I agree, it's important to consider the training data and language when selecting an ML Extractor. That's why Option B is the best practice.
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Devora
28 days ago
Option B is definitely the way to go. It's all about choosing the right ML Extractor for the specific document types and data quality.
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Sharee
2 months ago
I agree with Francine. The quality and diversity of the training data used to develop the ML Extractor is a key factor in determining its performance. Popularity and cost shouldn't be the primary drivers here.
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Rolland
26 days ago
Definitely, the popularity or cost shouldn't be the main factors in selecting an ML Extractor.
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Tandra
27 days ago
I agree, it's important to consider the document types and language when choosing an ML Extractor.
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Daniela
1 months ago
I think the quality and diversity of the training data is crucial for accurate extraction.
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Francine
2 months ago
Option B seems the most logical choice. Considering the document types, language, and data quality is crucial for ensuring accurate and reliable extraction results. The ML Extractor needs to be tailored to the specific use case, not just the most popular or cheapest one.
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Talia
1 months ago
Agreed, the quality and diversity of the training data used for the ML Extractor will impact the extraction results.
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Marylyn
1 months ago
Accuracy is crucial, so selecting a model optimized for the document types being processed is essential.
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Sheron
1 months ago
Definitely, choosing an ML Extractor based on specific needs is key. Popularity or cost shouldn't be the main factors.
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Thomasena
2 months ago
I agree, option B makes the most sense. We need to consider the document types and data quality for accurate results.
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Joseph
3 months ago
I prefer option D. Bigger models tend to perform better in my experience.
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Cristal
3 months ago
I agree with you, Alpha. Considering document types and data quality is crucial for accurate extraction.
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Alpha
3 months ago
I think option B is the best practice.
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