UiPath-SAIv1 Exam Dumps - UiPath Certified Professional Specialized AI Professional v1.0

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Question # 57

Where should a model be pinned in UiPath Communications Mining?

In the validation page.

In Admin > Models.

On the models tab.

When setting up a stream.

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Question # 58

Why might labels have bias warnings in UiPath Communications Mining, even with 100% precision?

They were trained using the "Search" option extensively.

They were trained using the "Shuffle" option extensively.

They have low recall.

They lack training examples.

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Answer:

Explanation:

Labels in UiPath Communications Mining are user-defined categories that can be applied to communications data, such as emails, chats, and calls, to identify the topics, intents, and sentiments within them1.Â Labels are trained using supervised learning, which means that users need to provide examples of data that belong to each label, and the system will learn from these examples to make predictions for new data2. However, not all labels are equally easy to train, and some may require more examples than others to achieve good performance.Â Labels that have bias warnings are those that have relatively low average precision, not enough training examples, or were labelled in a biased manner3. Precision is a measure of how accurate the predictions are for a given label, and it is calculated as the ratio of true positives (correct predictions) to the total number of predictions made for that label. A label with 100% precision means that all the predictions made for that label are correct, but it does not necessarily mean that the label is well-trained. It could be that the label has very few predictions, or that the predictions are only made on a subset of data that is similar to the training examples. This could lead to overfitting, which means that the label is too specific to the training data and does not generalize well to new or different data. Therefore, labels with 100% precision may still have bias warnings if they lack training examples, because this indicates that the label is not representative of the underlying data distribution, and may miss important variations or nuances that could affect the predictions. To improve the performance and reduce the bias of these labels, users need to provide more and diverse examples that cover the range of possible scenarios and expressions that the label should capture.

References:Â 1:Â Communications Mining OverviewÂ 2: [Creating and Training Labels]Â 3:Â Understanding and Improving Model PerformanceÂ : [Precision and Recall] : [Overfitting and Underfitting] :Â Fixing Labelling Bias With Communications Mining

Question # 59

If you need to retrieve an item based on a corresponding identifier in UiPath, which collection type would you use?

ArrayOf or System.DataType[] as it stores indexed values

System.Collections.Generic.Dictionary as it stores key-value pairs

System.Collections.Generic.Dictionary as it stores indexed values

System.Collections.Generic.List as it stores ordered values

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Question # 60

Which statement accurately describes out-of-the-box models in UiPath?

Out-of-the-box models are custom-built models created specifically for each project.

Out-of-the-box models are pre-trained models that cover a wide range of document types.

Out-of-the-box models require extensive training and customization before they can be used effectively.

Out-of-the-box models are only available for specific languages and cannot be adapted for multilingual documents.

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Question # 61

What is the definition of Deep Learning?

A sub-field of artificial intelligence that enables systems to learn from data.

Systems learn from previous experience and information to deduce and predict future information. To do this they use algorithms that learn to perform a specific task without being explicitly programmed.

The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.

A field of artificial intelligence that enables computers to gain high-level understanding from digital images or videos. If AI is the brain, then this is the eye that enables the computer to observe and understand. It works the same as the human eye.

An area of machine learning concerned with artificial neural networks.

These are a series of algorithms that aim to recognize relationships in a set of data through a process that mimics biological neural networks.

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