UiPath Certified Professional Specialized AI Professional Preparation Details
The UiPath-SAIv1 UiPath Certified Professional Specialized AI Professional exam validates hands-on skills in Document Understanding, Communications Mining, and AI Center. This study guide maps every official exam objective to current UiPath documentation so you can prepare efficiently. You can also explore more UiPath certification study guides on the UiPath Certification to keep building your skills.
UiPath Certified Professional Specialized AI Professional Materials
| Coursera | UiPath Automation Developer Professional |
| Udemy | UiPath Specialized AI Professional Practice Tests SAIv1 |
UiPath Document Understanding
Tasks performed by the Minimally Qualified Candidate:
Utilize the Document Understanding API.
Authentication and Authorization
Use the Classification Service
Utilize the DocPath LLM.
Describe the AI Trust Layer.
Describe the Cross Tennant functionalities.
Consuming models from different environments
Moving Document Understanding™ modern projects between tenants or organizations
Public endpoints for Automation Cloud and Test Cloud
UiPath Document Understanding Framework
Tasks performed by the Minimally Qualified Candidate:
Build POCs and automation components for DU template (but not a robust and complete DU process)
Document Understanding Process template
Create a new automation starting from a file
Invoices retrained with one additional field
Utilize the DU Process template to build a complete automation solution.
Document Understanding Process template
Automations in Document Understanding™
Utilize the business rules to validate the extracted data.
Use Action Center to incorporate human in the loop for end-to-end processes.
Document Classification Validation
Document Validation Components in Apps and Custom Apps
UiPath Studio – Document Understanding Activities
Tasks performed by the Minimally Qualified Candidate:
Explain what a Document Object Model is in the context of Document Understanding.
Select an appropriate OCR engine for your digitization use case.
Analyze what kind of classifier/extractor is the most suitable for the automation project.
Document classification overview
Use UiPath Orchestrator or Action Apps to configure human validation steps.
Document Validation Components in Apps and Custom Apps
Train the Classifiers and Extractors used to improve their performance.
Document classification training overview
Data Extraction Training overview
Machine Learning Extractor Trainer
Build the first model and test it out for labeling and types.
Evaluate the model.
Utilize the following Taxonomy features: Validator notes and business rules.
Utilize the Document Object Model in the context of Document Understanding.
Explain building prompts and the limitations for the Generative Classifier and the Generative Extractor.
UiPath Implementation Methodology – Document Understanding specific
Tasks performed by the Minimally Qualified Candidate:
Gather and analyze data about the documents in scope (document types, fields extracted, pages per documents).
Gather and analyze data about languages in scope and OCR engine of choice.
Integrate exception handling within the automation solution.
Document Understanding Process template
Automations in Document Understanding™
Document Understanding activities
UiPath AI Center
Tasks performed by the Minimally Qualified Candidate:
Distinguish between AI,ML,NLP,DL,Computer vision.
How machines learn to understand words: a guide to embeddings in NLP
Distinguish between supervised, unsupervised and reinforcement learning.
Active learning: better ML models in less time
Deep hierarchical unsupervised intent modelling
Describe how AI Center works.
List the user personas who can access and use AI Center.
Managing permissions at tenant level
Creating a new project and assigning users
List the types of ML models in AI Center.
Describe the various ways to deploy and install AI Center.
List the example of out-of-the box ML Packages application.
Define the AI Center User Interface elements.
Manage projects in AI Center (Create, edit, delete).
Creating a new project and assigning users
Manage datasets in AI Center (Create, upload, edit, delete, make the dataset public).
Manage data labels in AI Center (Create a data labeling instance, configure).
Using Data Labeling with human in the loop
Build ML Packages in AI Center.
Importing and exporting ML packages
Manage ML Packages in AI Center ( Upload ML Package, import, view ML Package details, version control of ML Packages).
Importing and exporting ML packages
Use Out-of-the-box ML Packages from AI Center.
Manage the Pipelines available in AI Center (Create, schedule pipelines, edit scheduled pipelines, remove pipelines).
Describe how to retrain a model by sending feedback from the process to the model.
Using Data Labeling with human in the loop
Create ML skills.
Invoke public ML Skills using API
Update ML Skills in new ML Packages ( Upload ML Package, import, view ML Package details, version control of ML Packages).
Update ML Skill deployment using API
Importing and exporting ML packages
Describe the steps to make an ML skill public.
Invoke public ML Skills using API
Describe the types of events captured in the ML logs.
UiPath Communications Mining – Model Training
Tasks performed by the Minimally Qualified Candidate:
Describe the golden rules of label training.
Model training and annotating best practice
Overview of the model training process
Describe the golden rules for general fields training.
Understanding labels, general fields, and metadata
Using the train tab
Generative annotation (cluster suggestions, assisted labelling)
Generative extraction (configuring extractions, generating extractions, training extractions, best practices, improving extraction performance)
Enabling Generative Extraction
Validating and annotating generated extractions
Best practices and considerations
Understanding validation on extractions and extraction performance
Describe the best practices of label annotation.
Model training and annotating best practice
Label hierarchy and best practices
Describe the best practices for general fields annotations.
Understanding labels, general fields, and metadata
Model training and annotating best practice
Describe the best practices for extraction fields annotations.
Best practices and considerations
Validating and annotating generated extractions
UiPath Communications Mining – Taxonomy Design
Tasks performed by the Minimally Qualified Candidate:
Create a label taxonomy structure according to best practices.
Building the taxonomy structure
Label hierarchy and best practices
Differentiate between analytics and automation taxonomies.
Comparing analytics and automation use cases
Provide examples of typical groups of labels (process/request types, quality of service / failure demand etc.).
Turning your objectives into labels
Building the taxonomy structure
Distinguish between different types of general fields (pre-trained, trained from scratch, trainable, non-trainable).
Understanding labels, general fields, and metadata
Describe Taxonomy Import Options
Quality of Service, Tone and Sentiment
Enabling sentiment on a dataset
Training with label sentiment analysis enabled
Labels (predictions, confidence levels, label hierarchy, and label sentiment)
UiPath Communications Mining – Setup
Tasks performed by the Minimally Qualified Candidate:
Describe the three main components of data (data sources, datasets, projects) and how to manage them.
Understanding the data structure and permissions
Enable, update, or disable general fields in a dataset in UiPath Communications Mining.
Import a taxonomy via the Settings or Train pages in UiPath Communications Mining.
Distinguish between tone analysis and label sentiment.
Enabling sentiment on a dataset
Training with label sentiment analysis enabled
Labels (predictions, confidence levels, label hierarchy, and label sentiment)
Role-based access control
Manage access and projects (Automation Cloud)
Managing access (Automation Cloud)
Data Architecture and Permissions.
Understanding the data structure and permissions
Understanding data requirements
Managing projects (Automation Cloud)
UiPath Communications Mining – Discover
Tasks performed by the Minimally Qualified Candidate:
Label clusters considering key best practices.
Model training and annotating best practice
Describe what the Search functionality in Discover is and when it is recommended to use it.
Explain the risks associated with using too much Search to train the model vs balancing out with Shuffle and Teach Label.
Training using Teach Label (Explore)
Model training and annotating best practice
Define what the Discover phase of the Communications Mining process, how it works, and why it’s important.
Overview of the model training process
Describe what clustering labels are and why they are important.
Describe the two main steps of the Discover phase
Generative Annotation functionality in Discover.
UiPath Communications Mining – Explore
Tasks performed by the Minimally Qualified Candidate:
Explain what label and general fields predictions are, how they work, and how to use them.
Labels (predictions, confidence levels, label hierarchy, and label sentiment)
Distinguish between label predictions and label suggestions.
Labels (predictions, confidence levels, label hierarchy, and label sentiment)
Differentiate between when it makes sense to use Shuffle, Teach Label, or Low Confidence to train the model in the ‘Explore’ phase.
Training using Teach Label (Explore)
Use Shuffle, Teach Label, or Low Confidence in the ‘Explore’ phase according to best practices.
Training using Teach Label (Explore)
Model training and annotating best practice
Explain when it is recommended to use Teach general fields to continue label training at the end of the Explore phase.
Training using Teach Label (Explore)
Prune and reorganize a taxonomy by editing, renaming, merging and deleting labels.
Refining and reorganizing your taxonomy
Prune and reorganize a taxonomy by modifying or deleting an general fields.
Refining and reorganizing your taxonomy
Define what Explore phase of the Communications Mining process, how it works, and why it is important.
Overview of the model training process
Describe the factors that indicate sufficient labeling training has been performed in the Explore phase.
When to stop training your model
Understanding and improving model performance
Generative Annotation functionality in Explore
Generative extraction.
Enabling Generative Extraction
Validating and annotating generated extractions
UiPath Communications Mining – Refine and Maintain
Tasks performed by the Minimally Qualified Candidate:
Define why the ‘Refine’ phase of the Communications Mining process is important.
Refining models and using Validation
Explain precision and recall metrics, how they impact the performance of machine learning models.
Precision and recall explained
Describe what Model Rating assesses, and what factors it takes into consideration (Performance, Coverage, Balance).
Understanding and improving model performance
Analyze All Labels and suggest typical solutions to improve the score (understand MAP).
Understanding and improving model performance
Reasons for label low average precision
Analyze Underperforming Labels and suggest typical solutions to improve the score.
Understanding and improving model performance
Reasons for label low average precision
Training using Check label and Missed label
Analyze Coverage to check how well covered the whole dataset is and suggest typical solutions to improve the score.
Understanding and increasing coverage
Understanding and improving model performance
Analyze Balance to check for a balanced representation of the whole dataset, and suggest typical solutions to improve the score.
Improving Balance and using Rebalance
Understanding and improving model performance
Distinguish between the three label performance indicators (blue, amber, red).
Understanding and improving model performance
List potential reasons that can lead to low label performance.
Reasons for label low average precision
Understanding and improving model performance
Fixing annotating bias with Communications Mining™
Address bias labelling by continuing to train the model using Teach Label in the ‘Refine’ phase of the Communications Mining process.
Training using Teach label (Refine)
Fixing annotating bias with Communications Mining™
Continue to train the model using Check Label and Missed Label in the ‘Refine’ phase of the Communications Mining process.
Training using Check label and Missed label
Refining models and using Validation
Name the recommended Model Ratings for automation and analytics use cases.
Comparing analytics and automation use cases
When to stop training your model
List indicators of when model training could be finished at the end of the UiPath Communications Mining process.
When to stop training your model
Understanding and improving model performance
Analyze what general fields scores are, how they are calculated, and typical solutions to improve them (Teach general fields, Check general fields, Missed general fields).
Understanding and improving model performance
List the two key factors that can be erode a model’s performance (brand new labels are added, but not trained, or concept drift occurs.)
Maintaining a model in production
Adding new labels to existing taxonomies
Explain how to add new labels to an existing taxonomy.
Adding new labels to existing taxonomies
Maintaining a model in production
Explain how models should be maintained in production
Maintaining a model in production
Model maintenance and using models
Publishing and tagging a model version
Use recommended next best actions in the Validation page to improve a training model.
Understanding and improving model performance
Describe what Mean Average Precision (MAP), why it is useful, and how to view it in the ‘Validation’ page.
It’s all in the numbers – assessing model performance with metrics
Analyze Metrics in the Validation page to check average label performance statistics.
It’s all in the numbers – assessing model performance with metrics
Why model validation is important
Use the ‘general fields’ tab to analyze validation statistics of the performance of all the general fields enabled in the dataset.
Understanding and improving model performance
Use the ‘extraction ‘ tab to analyze validation statistics of the performance of all the extraction fields enabled in the dataset.
Understanding validation on extractions and extraction performance
Analytics and Monitoring
Tasks performed by the Minimally Qualified Candidate:
Use the ‘Reports’ pages to create customized and dynamic dashboards.
Use the ‘Label Summary’ tab to analyze charts and high-level summary statistics.
Use the ‘Trends’ tab to analyze trends for verbatim volume, label volume, sentiment over a given time period, etc.
Use the ‘Segments’ tab to analyze label volumes versus verbatim metadata fields, e.g. Sender Domain.
Use the ‘Comparison’ tab to conduct A/B tests and cohort comparisons between different cohorts of the dataset.
Use the ‘Threads’ tab to analyze conversations and their characteristics.
Use ‘Quality of Service’ and ‘Tone Analysis’ to monitor channel performance.
Enabling sentiment on a dataset
Use ‘Alert Center’ to configure and track alerts and issues.
Automation and Model Management
Tasks performed by the Minimally Qualified Candidate:
Apply CI/CD best practices for model management.
Model maintenance and using models
Publishing and tagging a model version
Maintaining a model in production
View, create and modify streams.
Choosing the right threshold for streams.
Selecting label confidence thresholds
Precision and recall explained
Pinning model versions for productions and staging.
Publishing and tagging a model version
Describe the Dispatcher Framework
Communications Mining Studio Activities
Communications Mining Dispatcher Framework.
Quotas & Deprecated Models pages.
Publishing and tagging a model version
Explain how UiPath Communications Mining and RPA can work together
Selecting label confidence thresholds
Updates from product version 2023.xx and later
Tasks performed by the Minimally Qualified Candidate:
Use the Document Manager updates in your DU process.
Document types (Document Manager)
Define and apply business rules at field levels in Taxonomy Manager.
Use the DU Cloud APIs.
Authentication and Authorization
Use the Classification Service
Allocate roles using the new Role-based access control in Document Understanding.
Role based access control in Document Understanding™
Deprecations
UiPath Helix Extractor 1.0 [Deprecated]
Dataset import from Document Manager to Modern Projects
Project Performance dashboard
Field configuration experience
Classification Validation for Cross-platform Activities
Document Classification Validation
Classify documents automatically
Validator Notes
Extended OCR Engine & Arabic language support in UiPath DU OCR
Generative Extraction, Classification & Validation capabilities in APIs
FedRAMP
Public endpoints for Automation Cloud and Test Cloud Public Sector
Communications Mining release notes
Get attachments via Exchange integration
Troubleshooting Exchange integrations
Attachment property filters
Notification service integration for CM integrations & alerts
Troubleshooting Exchange integrations
Assignable user roles
Managing access (Automation Cloud)
Manage access and projects (Automation Cloud)
Communications Mining Dispatcher Framework
New dataset creation flow
Multilingual sources and datasets
Quotas & Deprecated Models pages
Publishing and tagging a model version
Advanced Developer Topics
Tasks performed by the Minimally Qualified Candidate:
Advanced Developer Topic
Document Understanding Process template
UiPath.IntelligentOCR.Activities
Automation Developer Topics
Tasks performed by the Minimally Qualified Candidate:
SharePoint integration, file manipulation, Microsoft 365
Automation Developer Generic
UiPath.IntelligentOCR.Activities
UiPath.DocumentUnderstanding.Activities
Wrapping Up UiPath Certified Professional Specialized AI Professional
This UiPath-SAIv1 guide covered all 16 exam sections, from Document Understanding and AI Center to Communications Mining and developer topics, with official documentation for each objective. Work through each section, practice hands-on in UiPath, and you will be ready for the Specialized AI Professional exam. You can also explore more UiPath certification study guides on the UiPath Certification to keep building your skills. Have a question or tip? Leave a comment below.
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