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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q52-Q57):

NEW QUESTION # 52
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?

A. The cost of the ML Extractor should be the main consideration when choosing an ML Extractor.
Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.
B. Consider the document types, language, and data quality when choosing an ML Extractor.
It is important to select one that is specifically trained or optimized for the document types being processed.
It is also important to take into account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.
C. The size of the ML Extractor is the most important factor to consider when choosing an ML Extractor.Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.
D. The popularity of the ML Extractor among other UiPath users should be the primary factor when choosing a UiPath ML Extractor.
Opt for the ML Extractor that has the highest number of downloads or positive reviews.

Answer: B

Explanation:
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

NEW QUESTION # 53
What components are part of the Document Understanding Process template?

A. Load Taxonomy, Digitization. Classification, Data Extraction, and Data Validation Export.
B. Import. Classification. Text Extractor, and Data Validation.
C. Load Document. Categorization. Data Extraction, and Validation.
D. Load Taxonomy, Digitization. Categorization. Data Validation, and Export.

Answer: A

Explanation:
The Document Understanding Process template is a fully functional UiPath Studio project template based on a document processing flowchart. It provides logging, exception handling, retry mechanisms, and all the methods that should be used in a Document Understanding workflow, out of the box. The template has an architecture decoupled from other connected automations and supports both attended and unattended processes with human-in-the-loop validation via Action Center. The template consists of the following components1:
* Load Taxonomy: This component loads the taxonomy file that defines the document types and fields to be extracted. The taxonomy file can be created using the Taxonomy Manager in Studio or the Data Manager web application.
* Digitization: This component converts the input document into a digital format that can be processed by the subsequent components. It uses the Digitize Document activity to perform OCR (optical character recognition) on the document and obtain a Document Object Model (DOM).
* Classification: This component determines the document type of the input document using the Classify Document Scope activity. It can use either a Keyword Based Classifier or a Machine Learning Classifier, depending on the configuration. The classification result is stored in a ClassificationResult variable.
* Data Extraction: This component extracts the relevant data from the input document using the Data Extraction Scope activity. It can use different extractors for different document types, such as the Form Extractor, the Machine Learning Extractor, the Regex Based Extractor, or the Intelligent Form Extractor. The extraction result is stored in an ExtractionResult variable.
* Data Validation: This component allows human validation and correction of the extracted data using the Present Validation Station activity. It opens the Validation Station window where the user can review and edit the extracted data, as well as provide feedback for retraining the classifiers and extractors. The validated data is stored in a DocumentValidationResult variable.
* Export: This component exports the validated data to a desired output, such as an Excel file, a database, or a downstream process. It uses the Export Extraction Results activity to convert the DocumentValidationResult variable into a DataTable variable, which can then be manipulated or written using other activities.
References: Document Understanding Process: Studio Template, Document Understanding Process - New Studio Template, Document Understanding Process Template in UiPath Studio

NEW QUESTION # 54
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. The purpose of the End Process in the Document Understanding Process is to generate a summary report of the processing statistics and performance metrics.
C. 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.
D. End Process is a feature in the Document Understanding Process that exports the extracted data into a readable document format.

Answer: C

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 # 55
When processing a document type that comes in a high variety of layouts, what is the recommended data extraction methodology?

A. Model-based data extraction.
B. Manual data extraction.
C. Hybrid data extraction.
D. Rule-based data extraction.

Answer: C

Explanation:
Based on the classification of documents, there are two common types of data extraction methodologies: rule- based data extraction and model-based data extraction1. Rule-based data extraction targets structured documents, while model-based data extraction is used to process semi-structured and unstructured documents1. However, neither of these methods alone can handle the high variety of layouts that some document types may have. Therefore, a hybrid data extraction approach is recommended, which combines the strengths of both methods and allows for more flexibility and accuracy23. A hybrid data extraction approach can use one or more extractors, such as RegEx Based Extractor, Form Extractor, Intelligent Form Extractor, Machine Learning Extractor, or FlexiCapture Extractor, depending on the document type and the fields of interest3. The Data Extraction Scope activity in UiPath enables the configuration and execution of a hybrid data extraction methodology, by allowing the user to customize which fields are requested from each extractor, what is the minimum confidence threshold for a given data point extracted by each extractor, what is the taxonomy mapping, at field level, between the project taxonomy and the extractor's internal taxonomy (if any), and how to implement "fall-back" rules for data extraction2.
References: 2: Data Extraction Overview 3: Data Extraction 1: Document Processing with Improved Data Extraction

NEW QUESTION # 56
Why is the Shuffle training mode important in the "Explore" phase while working with UiPath Communications Mining?

A. Because it helps create a balanced model by focusing on outlier labels with sufficient training examples but low confidence.
B. Because it creates a high precision model, focusing on labels with low precision and a low number of examples.
C. Because it creates a new model version with shuffled labels and general fields and lets the user explore to understand the state of the model
D. Because it helps create a balanced model free from labelling bias by introducing random variation into the training data.

Answer: D

Explanation:
The Shuffle training mode in the "Explore" phase of UiPath Communications Mining helps to introduce randomness into the training data. This prevents labeling bias by ensuring that the model does not overly focus on certain labels or examples, leading to a more balanced and generalized model. By shuffling the data, the model is exposed to a diverse range of examples, which enhances its ability to make accurate predictions across different labels and datasets.
(Source: UiPath Communications Mining documentation)

NEW QUESTION # 57
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