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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q152-Q157):
NEW QUESTION # 152
What is the purpose of the generative classifier?
Answer: C
Explanation:
Reference: UiPath Document Understanding Framework
NEW QUESTION # 153
What is the definition of a UiPath Communications Mining data source?
Answer: B
Explanation:
According to the UiPath documentation, a data source is a raw collection of verbatims, which are text-based communications such as survey responses, emails, transcripts, or calls1. A data source can be of a similar type and share a similar intended purpose, such as capturing customer feedbackor servicing requests2. A data source can be added to up to 10 different datasets, which are collections of sources and labels that are used to train and evaluate ML models3. Therefore, the correct definition of a UiPath Communications Mining data source is A.
References:
1: Communications Mining - Sources 2: Communications Mining - Managing Sources and Datasets 3: Communications Mining - Understanding the data structure and permissions
NEW QUESTION # 154
What information does the comparison between two cohorts display on the Comparison page in UiPath Communications Mining?
Answer: B
Explanation:
According to the UiPath documentation, UiPath Communications Mining is a tool that enables you to analyze text-based communications data, such as customer feedback, support tickets, or chat transcripts, using natural language processing (NLP) and machine learning (ML) techniques1. One of the features of UiPath Communications Mining is the Comparison page, which allows you to compare two cohorts of verbatims based on different criteria, such as date range, source, metadata, or label2. The Comparison page displays the following information for each cohort3:
Total verbatim count: The number of verbatims in the cohort.
Proportion for each label: The percentage of verbatims in the cohort that are assigned to each label. A label is a category or a topic that is relevant for the analysis, such as sentiment, intent, or issue type. Labels can be predefined or custom-defined by the user.
Statistical significance: The p-value that indicates whether the difference in proportions between the two cohorts is statistically significant or not. A p-value less than 0.05 means that the difference is unlikely to be due to chance.
The Comparison page also provides a visual representation of the proportions for each label using a bar chart, and allows the user to drill down into the verbatim content for each label by clicking on the bars3. Therefore, the correct answer is A.
References:
1: About Communications Mining 2: Communications Mining - Comparing Cohorts 3: Communications Mining - Comparison Page
NEW QUESTION # 155
In a UiPath Studio project, what is the broadest scope a variable can have?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Variables in UiPath are scoped to thecontainer (e.g., Sequence, Flowchart, or State Machine)where they are defined. Thebroadest scope possibleis theentire outermost workflow (e.g., Main.xaml).
* Global variables across multiple workflowsrequire use ofargumentsor storage inassets or config files
, not regular variables.
* UiPath Documentation Reference:Variables Panel - Scope Concept
NEW QUESTION # 156
Which of the following is an indicator that sufficient training has been completed for a model in UiPath Communications Mining?
Answer: A
Explanation:
The model rating is a proprietary score that assesses the overall health and performance of a model in UiPath Communications Mining. It considers four main factors: balance, underperforming labels, coverage, and all labels. The model rating is a score from 0 to 100, which equates to a rating of 'Poor' (0-49), 'Average' (50-
69), 'Good' (70-89) or 'Excellent' (90-100). A model rating of 70-90 or better indicates that the model has sufficient training and performs well in all of the most important areas. A model rating of 70-90 or better also means that the model has a balanced and representative training data, a low number of labels with performance issues or warnings, a high coverage of the dataset by informative labels, and a high average precision of all labels.
References: Communications Mining - Model Rating, Communications Mining - Understanding and improving model performance
NEW QUESTION # 157
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