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UiPath UiPath-SAIv1 Exam Sample Questions

Question # 1

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, Weekly, Monthly, Yearly.

C. Daily, Bi-weekly, Monthly, Yearly.

D. Daily, Bi-weekly, Quarterly, Yearly.

Answer

B. Daily, Weekly, Monthly, Yearly.

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.

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Question # 2

Which of the following is an indicator that sufficient training has been completed for a model in UiPath Communications Mining?

A. A model rating of 30-40.

B. A model rating of 40-50.

C. A model rating of 50-60.

D. A model rating of 70-90 or better.

Answer

D. A model rating of 70-90 or better.

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.

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Question # 3

Which of the following statements is true regarding reviewing and applying entities in UiPath Communications Mining?

A. A single entity value can be split across multiple paragraphs.

B. If the entity value is correctly predicted, but the entity type is wrong, it cannot be changed.

C. All of the entities within a paragraph should be reviewed.

D. All of the entities in a communication must be reviewed.

Answer

C. All of the entities within a paragraph should be reviewed.

Explanation: According to the UiPath Communications Mining documentation, reviewing and applying entities is a crucial step for improving the accuracy and performance of the entity extraction models. When reviewing entities, users should check all of the predicted entities within a paragraph, as well as any missing or incorrect ones. Users can accept, reject, edit, or create entities using the platform’s interface or keyboard shortcuts. Users can also change the entity type if the value is correct but the type is wrong. Reviewing and applying entities helps the platform learn from the user feedback and refine its predictions over time. It also helps users assess the automation potential and benefit of the communications data.

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Question # 4

What does adding missed labels help improve in UiPath Communications Mining?

A. Label bias warnings.

B. Increases data security.

C. Increases the taxonomy coverage.

D. Label precision and recall.

Answer

D. Label precision and recall.

Explanation: Adding missed labels helps improve the label precision and recall in UiPath Communications Mining. Precision is the percentage of correctly labeled verbatims out of all the verbatims that have the label applied, while recall is the percentage of correctly labeled verbatims out of all the verbatims that should have the label applied. By adding missed labels, you are increasing the recall of the label, as you are reducing the number of false negatives (verbatims that should have the label but do not). This also improves the precision of the label, as you are reducing the noise in the data and making the label more informative and consistent. Adding missed labels is one of the recommended actions that the platform suggests to improve the model rating and performance of the labels.

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Question # 5

What does the Export stage of the Document Understanding Framework do?

A. Converts the result of extraction to a dataset or to a customized format.

B. Allows a human to validate and correct the extracted data.

C. Classifies the document as one of the predefined document types.

D. Extracts the text out of the image document using OCR (Optical Character Recognition).

Answer

A. Converts the result of extraction to a dataset or to a customized format.

Explanation: According to the UiPath documentation portal1, the Export stage of the Document Understanding Framework is the final stage of the document processing workflow, where the extracted data is converted to a dataset or to a customized format, such as Excel, JSON, or XML. The Export stage enables you to easily export data for training ML models, using the Export files dialog box in Data Manager or Document Manager. The Export stage also allows you to export the schema of the fields and their configurations, which can be imported into a different session. The Export stage supports different export options, such as current search results, all labeled, schema, or all. The Export stage also provides validation rules, such as requiring at least 10 labeled documents or pages for each field, and at least one document for each classification option1. Therefore, option A is the correct answer, as it describes the main function and benefit of the Export stage. Option B is incorrect, as it refers to the Data Validation stage, which is the previous stage of the document processing workflow, where a human can review and correct the extracted data using the Validation Station or the Present Validation Station activities2. Option C is incorrect, as it refers to the Classification stage, which is the second stage of the document processing workflow, where the document is classified as one of the predefined document types using the Classify Document Scope activity and a classifier of choice3. Option D is incorrect, as it refers to the Digitization stage, which is the first stage of the document processing workflow, where the text is extracted out of the image document using OCR (Optical Character Recognition) using the Digitize Document activity and an OCR engine of choice.

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