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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q78-Q83):

NEW QUESTION # 78
What is the role of the Taxonomy Manager?

A. To present a document processing specific user interface for validating and correcting automatic classification outputs.
B. To select the type of ML that can be used in the project.
C. To select which extractors are trained for each document type and field.
D. To create and edit a Taxonomy file specific to the current automation project.

Answer: D

Explanation:
The Taxonomy Manager is a tool that enables you to create and edit a Taxonomy file, which is an XML file that defines the document types and fields that are relevant for your automation project1. The Taxonomy file is used by the Classify Document Scope and Data Extraction Scope activities to perform document classification and data extraction, respectively2. The Taxonomy Manager allows you to add, remove, rename, or reorder document types and fields, as well as specify the data type, format, and validation rules for each field3. The Taxonomy Manager also provides a preview of the Taxonomy file and a validation feature to check for errors or inconsistencies.
References:
1: About Taxonomy Manager 2: About Document Understanding Framework 3: Using the Taxonomy Manager : Taxonomy Manager User Interface Description

NEW QUESTION # 79
Which of the following time periods can be selected when viewing Trends in UiPath Communications Mining?
Which of the following time periods can be selected when viewing Trends in UiPath Communications Mining?

A. Daily, Monthly, Quarterly, Yearly.
B. Daily, Bi-weekly, Monthly, Yearly.
C. Daily, Bi-weekly, Quarterly, Yearly.
D. Daily, Weekly, Monthly, Yearly.

Answer: D

Explanation:
According to the UiPath Communications Mining documentation, the Trends tab in the Reports page displays charts for verbatim volume, label volume, and sentiment over a selected time period. Users can choose the time period for the data in the filter bar, and the time sequencing of the chart (i.e. daily, weekly, etc.) in the top right dropdown menu. The available options for the time sequencing are Daily, Weekly, Monthly, and Yearly. These options allow users to see how the trends change over different time intervals and identify patterns or anomalies.
References:
* Communications Mining - Using Reports
* Communications Mining - Trends

NEW QUESTION # 80
What is the recommended number of documents per vendor to train the initial dataset?

A. 0
B. 1
C. 2
D. 3

Answer: D

NEW QUESTION # 81
Which of these statements is true about precision and recall statistics for specific labels in UiPath Communications Mining precision?

A. The label precision and recall statistics are determined by the mean average precision (MAP).
B. The label precision and recall statistics are determined by the model's rating.
C. The label precision and recall statistics are determined by the confidence threshold.
D. The label precision and recall statistics are determined by the model's coverage.

Answer: C

Explanation:
In UiPath Communications Mining, the precision and recall statistics for specific labels are primarily determined by the confidence threshold set for the model. This threshold represents the level of certainty the model needs before it assigns a label to a message. By adjusting this threshold, you can trade off between precision (fewer false positives) and recall (fewer false negatives). For instance, a higher threshold will increase precision but may reduce recall, and vice versa

NEW QUESTION # 82
What is the recommended number of documents per vendor to train the initial dataset?

A. 0
B. 1
C. 2
D. 3

Answer: D

Explanation:
According to the UiPath documentation, the recommended number of documents per vendor to train the initial dataset is 10. This means that for each vendor that provides a specific type of document, such as invoices or receipts, you should have at least 10 samples of their documents in your training dataset. This helps to ensure that the dataset is balanced and representative of the real-world data, and that the machine learning model can learn from the variations and features of each vendor's documents. Having too few documents per vendor can lead to poor model performance and accuracy, while having too many documents from a single vendor can cause overfitting and bias1.
References: 1: Document Understanding - Training High Performing Models

NEW QUESTION # 83
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