Get UiPath-SAIv1 Dumps : Verified UiPath Certified Professional Specialized AI Professional v1.0

C:\Users\Waqas Shahid\Desktop\Mudassir\Untitled.jpg

to load a taxonomy from a given non-default location text file into a variable, the order of steps should be as follows:

• Create a new variable of type 'DocumentTaxonomy' called 'documentTaxonomy', then create a new string variable called 'taxonomyPath'.
• Using an Assign activity, assign the given non-default taxonomy text file path to the most recent variable created.
• Use the Read Text File activity on the 'taxonomyPath' variable created. Next, create a new variable 'taxonomyText', then save the output of the Read Text File activity to 'taxonomyText'.
• Inside the last Assign activity 'Value to Save' property add the following expression: "Newtonsoft.Json.JsonConvert.DeserializeObject(Of DocumentTaxonomy)(taxonomyText)" where 'taxonomyText' is the text read from the file and 'documentTaxonomy' (the most recent variable created) is the variable you created.

Following these steps in this order will load the taxonomy from a text file into the 'documentTaxonomy' variable in UiPath.

The Present Classification Station activity is used for classification validation in attended mode. It allows the user to review and correct the classification results of documents using the Classification Station interface. The other options are not suitable for attended mode, as they are either used for creating Action Center tasks (A and C) or for training classifiers (B).

References: Classification Station - UiPath Documentation Portal, Document Classification Validation Overview - UiPath Documentation Portal.

 Entity predictions refer to the process of identifying and highlighting a specific span of text within a communication that represents a value for a predefined entity type. For example, an entity type could be “Organization” and an entity value could be “UiPath”. Entity predictions are made by the platform based on the training data and the rules defined for each entity type. Users can review, accept, reject, or modify the entity predictions using the Classification Station interface12.

References: Communications Mining - Reviewing and applying entities, Communications Mining - Predictions - UiPath Documentation Portal.

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.

References: 1 Document Understanding - Export Documents 2 Document Understanding - Data Validation 3 Document Understanding - Classification Document Understanding - Digitization

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