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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 Oct. 29, 2026 (see below)
What is supervised learning?
Supervised learning is one of the most popular and widely used machine learning approaches. It involves providing the algorithm with labeled input/output pairs, which serve as examples of the desired behavior or outcome. The algorithm then learns a function that can generalize from these examples and make predictions for new, unseen data. Supervised learning can be used for tasks such as classification, regression, and anomaly detection. Some common supervised learning algorithms are linear regression, logistic regression, decision trees, support vector machines, and neural networks.
References:
UiPath AI Fabric - Machine Learning Concepts
UiPath Document Understanding - Machine Learning Models
UiPath Communications Mining - Overview
What can the Sentiment Analysis out-of-the-box model be used for?
The Sentiment Analysis out-of-the-box model in UiPath is used to interpret and classify the emotional tone within text data. It is particularly useful for analyzing product reviews, customer feedback, social media posts, and emails to understand customer sentiment and emotional responses, helping businesses make informed decisions based on these insights. (Source: UiPath Sentiment Analysis model documentation
Which of the following file types are supported for the DocumentPath property in the Classify Document Scope activity?
According to the UiPath documentation portal1, the DocumentPath property in the Classify Document Scope activity accepts the path to the document you want to validate. This field supports only strings and String variables. The supported file types for this property field are .png, .gif, .jpe, .jpg, .jpeg, .tiff, .tif, .bmp, and .pdf. Therefore, option B is the correct answer, as it contains four of the supported file types. Option A is incorrect, as .psd is not a supported file type. Option C is incorrect, as .raw is not a supported file type. Option D is incorrect, as .eps is not a supported file type.
References:1Activities - Classify Document Scope - UiPath Documentation Portal
For which version(s) from Out-of-the-Box ML Packages minor versions is the download functionality available?
The download functionality for Out-of-the-Box ML Packages is available starting from Version 1 and above. This ensures access to the latest pretrained models and minor version updates to improve performance.
How can a Pipeline be scheduled?
In UiPath's AI Center, a Pipeline can be scheduled for execution either at a specific future date or with a recurring schedule. This allows for flexibility in automating the retraining or execution of machine learning models based on predefined time intervals or specific dates. For example, you can schedule a pipeline to run once on a given date or set it to run daily, weekly, or monthly, depending on the project's needs.
For more information, refer to:
UiPath AI Center Documentation: Pipeline Scheduling
Automation Pipelines in AI Center: Pipeline Execution and Scheduling
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