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UiPath-SAIv1 Exam Questions
- Topic 1: UiPath Document Understanding Framework: In this topic, students will explore the UiPath Document Understanding Framework, which includes building POCs and automation components for the Document Understanding template, but not a complete and robust process.
- Topic 2: UiPath Studio - Document Understanding Activities: This topic covers various activities within UiPath Studio related to Document Understanding. Students will delve into the Document Object Model, OCR engine selection, classifier and extractor analysis.
- Topic 3: Document Understanding Specific UiPath Implementation Methodology: In this topic, students will learn about the specific methodologies for implementing Document Understanding in UiPath. They will cover data gathering and analysis for documents, languages, and OCR engines, as well as integrating exception handling within the automation solution. UiPath AI Center: This topic focuses on the UiPath AI Center. Students will distinguish between AI, ML, NLP, DL, and computer vision, and between various types of learning. They will also learn about AI Center functionality, user personas, ML model types, and deployment methods.
- Topic 4: UiPath Communications Mining - Model Training: In this topic, students will explore model training within UiPath Communications Mining. They will learn about the golden rules of label and general fields training, using the train tab, generative annotation, and extraction.
- Topic 5: UiPath Communications Mining - Taxonomy Design: This topic covers taxonomy design in UiPath Communications Mining. Students will create label taxonomies, differentiate between analytics and automation taxonomies, and learn about typical groups of labels and types of general fields.
- Topic 6: UiPath Communications Mining - Setup: In this topic, students will learn about setting up UiPath Communications Mining. They will cover managing data components, enabling and updating general fields, importing taxonomies, and distinguishing between tone analysis and label sentiment.
- Topic 7: UiPath Communications Mining - Discover: This topic focuses on the discovery phase of UiPath Communications Mining. Students will learn about labeling clusters, search functionality, and balancing search with shuffle and teach label.
- Topic 8: UiPath Communications Mining - Explore: In this topic, students will explore label and entity predictions within UiPath Communications Mining. They will distinguish between label predictions and suggestions, and understand when to use shuffle, teach label, or low confidence training.
- Topic 9: UiPath Communications Mining - Refine and Maintain: This topic covers refining and maintaining models in UiPath Communications Mining. Students will learn about the importance of the refine phase, precision and recall metrics, model rating assessments, and typical solutions to improve scores for labels, coverage, and balance.
- Topic 10: Analytics & Monitoring: In this topic, students will explore analytics and monitoring within UiPath. They will learn to use reports to create dashboards, analyze charts and summary statistics, and assess trends and segments. These sub-topics are crucial for effectively monitoring and analyzing data within UiPath.
- Topic 11: Updates introduced to 2023.10: This topic covers the updates introduced in UiPath's 2023.10 version. Students will learn about document manager updates, business rules in taxonomy manager, document understanding cloud APIs, deprecations, dataset import from document manager, and field configuration experiences.
Free UiPath UiPath-SAIv1 Exam Actual Questions
Note: Premium Questions for UiPath-SAIv1 were last updated On Jul. 26, 2025 (see below)
Why is the Shuffle training mode important in the "Explore" phase while working with UiPath Communications Mining?
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)
What are all the types of ML (Machine Learning) models supported by Al Center?
In UiPath AI Center, the platform supports several types of machine learning (ML) models, including:
Out-of-the-box models from UiPath: Pre-built models designed for common automation tasks.
Models from UiPath technology partners: External models developed by UiPath's partners.
Open-source models: Community-contributed models that can be used and adapted for various use cases.
Custom models: Models that users build and train specifically for their projects using their datasets.
This flexibility in model support ensures that organizations can leverage a wide range of machine learning capabilities to suit different automation needs.
For more details, refer to:
UiPath AI Center Documentation: AI Center Models
Machine Learning Model Types: Types of Models in UiPath AI Center
Which environment variable is relevant for Evaluation pipelines?
The environment variable eval.redo_ocr is relevant for Evaluation pipelines because it allows you to rerun OCR when running the pipeline to assess the impact of OCR on extraction accuracy. This assumes an OCR engine was configured when the ML Package was created. The other options are not valid environment variables for Evaluation pipelines.
References:Document Understanding - Evaluation Pipelines
When creating a training dataset, what is the recommended number of samples for the Classification fields?
For an analytics use case, what are the recommended minimum model performance requirements in UiPath Communications Mining?
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