UiPath UiPath-SAIv1 Practice Test Questions Answers

According to the UiPath documentation, the recommended number of samples for the classification fields depends on the number of document types and layouts that you want to classify. The more document types and layouts you have, the more samples you need to cover the diversity of your data. However, a general guideline is to have at least 20-50 document samples from each class, as this would provide enough data for the classifiers to learn from12. A large number of samples per layout is not mandatory, as the classifiers can generalize from other layouts as well3.

References: 1: Document Classification Training Overview 2: Document Classification Training Related Activities 3: Training High Performing Models

 The ML Extractor is a data extraction tool that uses machine learning models provided by UiPath to identify and extract data from documents. The ML Extractor can work with predefined document types, such as invoices, receipts, purchase orders, and utility bills, or with custom document types that are trained using the Data Manager and the Machine Learning Classifier Trainer12.

According to the best practice, the ML Extractor should be chosen based on the document types, language, and data quality of the documents being processed. It is important to select an ML Extractor that is specifically trained or optimized for the document types that are relevant for the use case, as different document types may have different layouts, fields, and formats. It is also important to take into account the language of the documents, as some ML Extractors may support only certain languages or require specific language settings. Moreover, it is important to consider the quality and diversity of the training data used to train the ML Extractor, as this may affect the accuracy and reliability of the extraction results. The training data should be representative of the real-world data, and should cover various scenarios, variations, and exceptions3.

References: 1: Machine Learning Extractor - UiPath Activities 2: Machine Learning Classifier Trainer - UiPath Document Understanding 3: Data Extraction - UiPath Document Understanding

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'.

This step involves setting up the necessary variables that will be used in the process. The 'documentTaxonomy' variable will hold the deserialized taxonomy object, and 'taxonomyPath' will store the path to the taxonomy file.

Using an Assign activity, assign the given non-default taxonomy text file path to the most recent variable created.

Here you will assign the path of the taxonomy file to the 'taxonomyPath' variable.

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'.

This step is where you read the contents of the taxonomy file using the 'Read Text File' activity. The contents are stored in the 'taxonomyText' variable.

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.

In this final step, you will deserialize the JSON content from the 'taxonomyText' into a 'DocumentTaxonomy' object using the 'JsonConvert.DeserializeObject' method and assign it to the 'documentTaxonomy' variable.

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