UiPath-SAIv1 Exam Dumps - UiPath Certified Professional - General Track Certification Questions and Answers

When creating an ML Package in UiPath AI Center, the package name must adhere to Python naming conventions, meaning it cannot include reserved keywords such as class, break, finally, etc. This ensures that the package can be successfully deployed without conflicts in the Python environment.

The Configure Extractors Wizard is a tool that enables you to select and customize the extractors that are used for data extraction from documents. It is accessed via the Data Extraction Scope activity, which is a container for extractor activities. The wizard allows you to map the fields defined in your taxonomy with the fields supported by each extractor, and to set the minimum confidence level and the framework alias for each extractor. The wizard is mandatory for the extractor configuration, as it ensures that the extractors are applied correctly to each document type and field1.

References: Configure Extractors Wizard of Data Extraction Scope

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.

UiPath AI Center offers three primary package types: pre-trained, custom training, and fine-tunable models. These are essential for various use cases, from leveraging existing models to training custom ones based on specific data

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)

The RegEx Based Extractor is most effective for documents where data fields follow a strict and predictable format, such as dates, invoice numbers, or specific patterns like email addresses. This method is well-suited for extracting information from structured or semi-structured documents with consistent formats across different documents, making it highly reliable for use cases where patterns are easily identifiable and do not vary significantly.

(Source: UiPath Document Understanding documentation)

In the UiPath 2023.10 release, a key improvement introduced was the ability to record and store failed queue transactions for debugging purposes. This feature helps users by capturing the failure details and making them accessible directly in Orchestrator. The recording of failed transactions is enabled at the process level, within the Video section of the Job recording settings. It is intended to facilitate easier troubleshooting of failed automations by providing insights into the moments leading to the failure, which can be reviewed later for debugging

The correct pipeline configuration for creating a new model version that includes both the initial 1000 pages and the newly labeled 800 pages is to run a Full pipeline. This ensures that the model is fully retrained with all available data (1800 pages in total). The evaluation dataset should remain the 100 pages to gauge the model's performance after retraining. This approach maximizes the model's learning by incorporating all labeled data while allowing for evaluation against a known dataset.

The Images folder in the exported dataset from UiPath's Document Manager contains each page of the documents in the dataset saved as images. This ensures that the visual layout of the documents is preserved, which is especially important when working with scanned documents or files that need to retain their original format for processing or validation purposes​