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UiPath-SAIv1 Exam - Topic 8 Question 37 Discussion

When creating a training dataset, what is the recommended number of samples for the Classification fields?
C) 20-50 document samples from each class.
A) 5-10 document samples from each class.
B) 10-20 document samples from each class.
D) 50-200 document samples from each class.

UiPath-SAIv1 Exam - Topic 8 Question 37 Discussion

Actual exam question for UiPath's UiPath-SAIv1 exam
Question #: 37
Topic #: 8
[All UiPath-SAIv1 Questions]

When creating a training dataset, what is the recommended number of samples for the Classification fields?

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Suggested Answer: C

According to the UiPath documentation, the recommended number of samples for the classification fields depends on the number of document types and layouts that you want to classify. The more document types and layouts you have, the more samples you need to cover the diversity of your data.However, a general guideline is to have at least 20-50 document samples from each class, as this would provide enough data for the classifiers to learn from12.A large number of samples per layout is not mandatory, as the classifiers can generalize from other layouts as well3.

References:1: Document Classification Training Overview2: Document Classification Training Related Activities3: Training High Performing Models


by Judy at Apr 29, 2025, 11:34 AM

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Ailene
7 months ago
More samples usually mean better accuracy, right?
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Coleen
7 months ago
Definitely agree with option C!
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Kenneth
7 months ago
Wait, 50-200 samples? That seems excessive!
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Teddy
7 months ago
I thought 10-20 was enough for basic models.
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Veronique
8 months ago
I've read that 20-50 samples is a good range.
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Graciela
8 months ago
I recall reading that 20-50 samples is often recommended, but I wonder if it varies by the complexity of the classes.
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Garry
8 months ago
I’m a bit confused; I thought it was around 10-20 samples, but that seems low for effective training.
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Julio
8 months ago
I practiced a question similar to this, and I feel like 50-200 samples might be the right range for good classification.
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Lawana
9 months ago
I think I remember something about needing at least 20 samples per class, but I'm not entirely sure.
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Queenie
9 months ago
Okay, I've got this. The key is to have enough samples to capture the variability in each class, but not so many that the model gets too complex. I'd say 10-20 samples per class is a solid sweet spot.
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Tyisha
9 months ago
I'm a bit confused on this one. I know more data is generally better, but I'm not sure if that applies the same way for classification tasks. I'll have to review my notes.
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Carlota
9 months ago
I'm pretty sure the recommended range is 20-50 samples per class. That seems like a good balance between having enough data and not overfitting.
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Alease
9 months ago
Hmm, this is a tricky one. I'll need to think carefully about the trade-offs between sample size and model complexity.
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