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UiPath-SAIv1 Schulungsmaterialien & UiPath-SAIv1 Dumps Prüfung & UiPath-SAIv1 Studienguide

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UiPath UiPath-SAIv1 Demotesten, UiPath-SAIv1 Fragenkatalog

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UiPath Certified Professional Specialized AI Professional v1.0 UiPath-SAIv1 Prüfungsfragen mit Lösungen (Q105-Q110):

105. Frage
What is the relationship between AI Center and UiPath Document Understanding?

A. AI Center is the infrastructure on top of which UiPath Document Understanding machine learning models run.
B. Document Understanding is the infrastructure on which AI Center digitization runs.
C. AI Center is the infrastructure on top of which UiPath Document Understanding digitization runs.
D. Document Understanding is the infrastructure on which AI Center machine learning models run.

Antwort: A

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106. Frage
What is supervised learning?

A. Supervised learning is a machine learning paradigm that refers to algorithms that learn patterns from unlabeled data.There are only input variables, but no corresponding output variables. The goal of the algorithm is to model the underlying structure of the data, but there are no correct answers and no teachers.
B. Supervised learning is a machine learning paradigm with the goal of learning a function that maps input variables with output variables.
In every case there is a correct answer, so the aim is to train the model until it reaches an acceptable level of performance in predicting the outcome, at which point the learning stops.
C. Supervised learning is a machine learning paradigm in which algorithms try to solve a problem only by trial and error and using a system of rewards and punishments.
There is no need for labeled input/output pairs to be presented. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).
D. Supervised learning is a machine learning paradigm in which algorithms try to solve a problem in an uncertain, potentially complex environment only by trial and error and using a system of rewards and punishments.
There are no correct answers, but feedback is given in the form of rewards and penalties.

Antwort: B

Begründung:
Supervised learning is one of the most popular and widely used machine learning approaches. It involves providing the algorithm with labeled input/output pairs, which serve as examples of the desired behavior or outcome. The algorithm then learns a function that can generalize from these examples and make predictions for new, unseen data. Supervised learning can be used for tasks such as classification, regression, and anomaly detection. Some common supervised learning algorithms are linear regression, logistic regression, decision trees, support vector machines, and neural networks.
References:
* UiPath AI Fabric - Machine Learning Concepts
* UiPath Document Understanding - Machine Learning Models
* UiPath Communications Mining - Overview

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107. Frage
What does the darker shading of a label prediction represent in Explore in UiPath Communications Mining?

A. Multiple label predictions.
B. An incorrect prediction.
C. A higher confidence score.
D. A lower confidence score.

Antwort: C

Begründung:
In UiPath Communications Mining, the shading of label predictions in the Explore section indicates confidence levels. Darker shading represents a higher confidence score, meaning the model is more certain of the prediction made for that particular label

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108. Frage
A developer needs to create a process for the Human Resources team. During the development, they try to run the workflow containing the following dictionary variable:

What is the possible cause of the error?

A. The assign's set value syntax should be PinMapping<"John Doe">.
B. The Dictionary was not initialized.
C. The "John Doe" key was not present in the dictionary.
D. The assign's set value syntax should be PinMapping["John Doe"].

Antwort: B

Begründung:
The most likely cause of the error is that the dictionary was not initialized. In UiPath, a dictionary must be initialized before assigning values to its keys. If you attempt to add a key-value pair to a dictionary that has not been initialized, you will encounter a runtime error.
The correct initialization can be done as follows:
PinMapping = New Dictionary(Of String, String)
Explanation of Other Options:
* A. PinMapping["John Doe"]: This is the correct syntax for accessing or assigning a value to a dictionary key. There is no issue with this syntax.
* B. The "John Doe" key not present: While it is true that the key might not exist, this specific error would occur only during a Get operation, not an assignment.
* D. PinMapping<"John Doe">: This is incorrect syntax for working with dictionaries in UiPath.

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109. Frage
What does the Train stage of the Document Understanding Framework do?

A. Improves the extractor accuracy by learning from the classification result.
B. Allows the extractor to improve its prediction over time by using better OCR (Optical Character Recognition) engines.
C. Allows the model to learn from human-validated data.
D. Allows a human to validate and correct the extracted data.

Antwort: C

Begründung:
In the UiPath Document Understanding Framework, the Train stage enables models to learn from human- validated data. This process involves feeding the corrections made by humans during the validation phase back into the model, allowing it to refine its predictions and improve accuracy over time.
UiPath Documentation
The training component is crucial for classifiers and extractors capable of learning from human feedback. By incorporating validated data, these components can adjust their algorithms to better handle similar documents in the future, enhancing the overall efficiency and effectiveness of the automation process.
Other options are incorrect because:
* B. Allows the extractor to improve its prediction over time by using better OCR engines: While better OCR engines can enhance data extraction, this is not the function of the Train stage.
* C. Allows a human to validate and correct the extracted data: This describes the Validation stage, not the Train stage.
* D. Improves the extractor accuracy by learning from the classification result: Training focuses on learning from human-validated extraction results, not just classification outcomes.
Therefore, the primary purpose of the Train stage is to allow the model to learn from human-validated data, thereby improving its future performance.

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110. Frage
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