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Latest UiPath UiPath-SAIAv1 Study Notes | Online UiPath-SAIAv1 Version

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q246-Q251):

NEW QUESTION # 246
How do partially labeled messages impact label predictions in UiPath Communications Mining?

A. They reduce label bias.
B. They enhance label recall.
C. They improve label precision.
D. They negatively affect model performance.

Answer: D

Explanation:
Partially labeled messages in UiPath Communications Mining can negatively impact model performance because incomplete or inconsistent labeling creates ambiguity for the model during training. This leads to the model receiving conflicting signals, which hampers its ability to generalize well across the dataset, resulting in reduced precision and recall in predictions. Properand complete labeling is critical to ensure the model learns accurately from the data

NEW QUESTION # 247
What should a UiPath Communications Mining taxonomy contain when it is being imported?

A. Label descriptions.
B. Entity descriptions.
C. Entity predictions.
D. Label predictions.

Answer: A

Explanation:
According to the UiPath documentation, a UiPath Communications Mining taxonomy is a collection of all the labels applied to the verbatims in a dataset, structured in a hierarchical manner. Labels are the concepts and intents that you want to capture in the dataset to suit your specific objectives. When you import your taxonomy from a spreadsheet, you need to provide the label descriptions, which are the names of the labels and their level in the hierarchy. Label predictions and entity predictions are the outputs of the model training process, and they are not part of the taxonomy. Entity descriptions are the definitions of the entities that you want to extract from the verbatims, and they are not part of the taxonomy either.
References:
Communications Mining - Taxonomies
Communications Mining - Importing your taxonomy
Communications Mining - Building your taxonomy structure

NEW QUESTION # 248
What is the purpose of the End Process in the Document Understanding Process?

A. End Process in the Document Understanding Process silently shuts down the Virtual Machine so that another robot can use it.
B. End Process sets the queue transaction status as Successful in case of no exception, and as Failed in case of an exception with their corresponding Business or System Exception, and the post processing
/cleaning if required.
C. The purpose of the End Process in the Document Understanding Process is to generate a summary report of the processing statistics and performance metrics.
D. End Process is a feature in the Document Understanding Process that exports the extracted data into a readable document format.

Answer: B

Explanation:
The End Process is the final stage of the Document Understanding Process, which is a fully functional UiPath Studio project template based on a document processing flowchart. The End Process is responsible for setting the queue transaction status, logging the results, and performing any post processing or cleaning actions if needed. The End Process sets the queue transaction status as Successful if the document was processed without any exception, and as Failed if an exception occurred, either a Business Exception (such as invalid data) or a System Exception (such as network failure). The End Process also adds the extracted data and the validation status as output arguments to the queue transaction. The End Process also logs the processing statistics, such as the number of documents processed, the number of exceptions, the average processing time, and the accuracy rate. The End Process also performs any post processing or cleaning actions, such as deleting temporary files, closing applications, or sending notifications1.
References: 1: Document Understanding Process: Studio Template

NEW QUESTION # 249
What information does the comparison between two cohorts display on the Comparison page in UiPath Communications Mining?

A. Entity count for each metadata.
B. Total verbatim count and proportion for each label.
C. Verbatim content for each label.
D. Differences in verbatim length between Group A and Group B.

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 # 250
What is the primary advantage of the One Click Extraction feature in UiPath's Document Understanding interface?

A. It allows training document extractors without manually creating Datasets and Pipelines, but you need to manually deploy the ML Skills in AI Center.
B. It provides a simplified way to manually create Datasets, Pipelines, and Datasets for document extraction.
C. It automates the creation of document extractors by automatically creating Datasets, Pipelines, and ML Skills but requires manual creation of Pipelines.
D. It allows training document extractors without manually creating Datasets, Pipelines, and ML Skills in AI Center.

Answer: D

Explanation:
Reference: UiPath One Click Extraction

NEW QUESTION # 251
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