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UiPath-SAIv1 Practice Questions

Question # 1
Which technology enables UiPath Communications Mining to analyze and enable action on messages?
A. Natural Language Processing (NLP)
B. Virtual Reality.
C. Cloud Computing.
D. Robotic Process Automation


A. Natural Language Processing (NLP)

Explanation: UiPath Communications Mining is a new capability to understand and automate business communications. It uses state-of-the-art AI models to turn business messages—from emails to tickets—into actionable data. It does this in real time and on all major business communications channels1. Natural Language Processing (NLP) is the branch of AI that deals with analyzing, understanding, and generating natural language. NLP enables UiPath Communications Mining to extract the most important data from any message, such as reasons for contact, data fields, and sentiment2. NLP also allows UiPath Communications Mining to deploy custom AI models in hours, not weeks, by using automatic labeling and annotation2.


Question # 2
What information should be filled in when adding an entity label for the OOB (Out Of the Box) labeling template?
A. Name. Data Type. Attribute name, and Color.
B. Name, Data Type. Attribute name. Shortcut, and Color.
C. Name, Shortcut, and Color.
D. Name. Input to be labeled. Attribute name. Shortcut, and Color.


D. Name. Input to be labeled. Attribute name. Shortcut, and Color.

Explanation: The OOB labeling template is a predefined template that you can use to label your text data for entity recognition models. The template comes with some preset labels and text components, but you can also add your own labels using the General UI or the Advanced Editor. When you add an entity label, you need to fill in the following information:
Name: the name of the new label. This is how the label will appear in the labeling tool and in the exported data.
Input to be labeled: the text component that you want to label. You can choose from the existing text components in the template, such as Date, From, To, CC, and Text, or you can add your own text components using the Advanced Editor. The text component determines the scope of the text that can be labeled with the entity label.
Attribute name: the name of the attribute that you want to extract from the text. You can use this to create attributes such as customer name, city name, telephone number, and so on. You can add more than one attribute for the same label by clicking on + Add new.
Shortcut: the hotkey that you want to assign to the label. You can use this to label the text faster by using the keyboard. Only single letters or digits are supported. Color: the color that you want to assign to the label. You can use this to distinguish the label from the others visually.


Question # 3
Which activity should be used for classification validation in attended mode?
A. Create Document Classification Action.
B. Train Classifiers Scope.
C. Wait for Document Classification and Resume.
D. Present Classification Station.


D. Present Classification Station.

Explanation: 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).


Question # 4
While training a UiPath Communications Mining model, the Search feature was used to pin a certain label on a few communications. After retraining, the new model version starts to predict the tagged label but infrequently and with low confidence.
According to best practices, what would be the correct next step to improve the model's predictions for the label, in the "Explore" phase of training?
A. Use the "Rebalance" training mode to pin the label to more communications.
B. Use the 'Teach" training mode to pin the label to more communications.
C. Use the "Low confidence" training mode to pin the label to more communications.
D. Use the "Search" feature to pin the label to more communications.


B. Use the 'Teach" training mode to pin the label to more communications.

Explanation: According to the UiPath documentation, the ‘Teach’ training mode is used to improve the model’s predictions for a specific label by pinning it to more communications that match the label’s criteria. This helps the model learn from more examples and increase its confidence and accuracy. The ‘Teach’ mode also allows you to unpin the label from communications that do not match it, which helps the model avoid false positives. The other training modes are not as effective for this purpose, as they either focus on different aspects of the model performance or do not provide enough feedback to the model.


Question # 5
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?
A. 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.
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 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.
D. 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.


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.

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


Question # 6
Which of the following consumes Page Units?
A. Applying OCR on a 10-page document.
B. Creation of a Document Validation Action in Action Center.
C. Using ML Classifier on a 21-page document.
D. Using Intelligent Form Extractor on a 5-page document with 0 successful extractions.


A. Applying OCR on a 10-page document.

Explanation: According to the UiPath documentation, Page Units are the measure used to license Document Understanding products. Page Units are charged based on the number of pages processed by the Document Understanding models, such as extractors, OCR engines, and classifiers. Therefore, applying OCR on a 10-page document consumes Page Units, while the other options do not. The creation of a Document Validation Action in Action Center does not consume any Page Units, as it is a human-in-the-loop activity. Using ML Classifier on a 21-page document does not consume Page Units, as it is a free model. Using Intelligent Form Extractor on a 5-page document with 0 successful extractions does not consume Page Units, as the extractor only charges for successful extractions.


Question # 7
Can you use Queues in the Document Understanding Process?
A. The Document Understanding Process can't use Queues because items waiting for Human Validation for more than 10 days will be marked as Abandoned.
B. The Document Understanding Process can use Queues but the Auto Retry Functionality should be disabled.
C. The Document Understanding Process can use Queues but the Auto Retry Functionality should be enabled.
D. The Document Understanding Process can't use Queues because items waiting for Human Validation for more than 24h will be marked as Abandoned.


B. The Document Understanding Process can use Queues but the Auto Retry Functionality should be disabled.

Explanation: The Document Understanding Process is a fully functional UiPath Studio project template based on a document processing flowchart. It supports both attended and unattended robots with human-in-the-loop validation via Action Center. The process uses queues to store and process the input files, one file per queue item. However, the Auto Retry Functionality should be disabled on queues, because it can interfere with the human validation step and cause errors or duplicates. The process handles the retry mechanisms internally, using the Try/Catch and Error management features.


Question # 8
What is the purpose of the "Explore" phase in UiPath Communications Mining?
A. To fully review and correctly tag the model version, regardless if it's "Live" or "Staging".
B. To use the bulk label functionality, a helpful tool to quickly train the model when searching for specific terms.
C. To provide each label/entity in a taxonomy with enough training examples so the model can make accurate predictions at scale.
D. To review the clusters of similar communications from a data set that unsupervised learning automatically found.


C. To provide each label/entity in a taxonomy with enough training examples so the model can make accurate predictions at scale.

Explanation: The Explore phase is the second phase of model training in UiPath Communications Mining, which is a solution that enables the analysis of large volumes of text-based communications using natural language processing and machine learning. The Explore phase builds on the foundations of the taxonomy that was created in the Discover phase by reviewing clusters and searching for different terms and phrases. The objective of the Explore phase is to provide each of the labels or entities that are important for the use case with enough varied and consistent training examples, so that the platform has sufficient training data from which to make accurate predictions across the entire dataset. The Explore phase is the core phase of model training, and requires the most time and effort, but also leads to better model performance and accuracy1.


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UiPath UiPath-SAIv1 Exam Dumps

Exam Name: UiPath Certified Professional Specialized AI Professional v1.0
Certification Name: UiPath Certified Professional - Developer Track

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  • Last Updation Date: 17-Mar-2025

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