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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic	Details
Topic 1	Debugging: This section of the exam measures skills of automation analysts and covers debugging within Document Understanding workflows. It explores the template’s architecture, exception handling, validation steps, and post-processing techniques that ensure accuracy and fault tolerance.
Topic 2	UiPath Communications Mining - Model Training: This section of the exam measures skills of automation analysts and covers model training concepts in Communications Mining, explaining what defines a strong model and outlining the stages and components involved in developing one.
Topic 3	Implementation Methodology: This section of the exam measures skills of automation analysts and covers project lifecycle knowledge, understanding key stages of implementation, and interpreting Process Design Documents (PDDs) and Solution Design Documents (SDDs).
Topic 4	UiPath Communications Mining: This section of the exam measures skills of RPA developers and covers the application of Communications Mining in automation and analytics. It distinguishes this capability from Task Mining and Process Mining, explains the interface, and describes use cases.
Topic 5	Version Control Integration: This section of the exam measures skills of automation analysts and covers the use of Git integration in UiPath Studio for source control, including committing changes, cloning repositories, and pushing updates in collaborative environments.
Topic 6	Updates Introduced to 2023.10: This section of the exam measures skills of automation analysts and covers the most recent product updates in UiPath, including one-click classification and extraction, Generative AI features, and enhancements to validation, annotation, and workflow design.
Topic 7	UiPath Document Understanding Framework: This section of the exam measures skills of automation analysts and covers how to apply the Document Understanding Framework, use templates, and develop proof-of-concept components. It focuses on building workflows for document processing.
Topic 8	UiPath Studio - Document Understanding Activities: This section of the exam measures skills of RPA developers and covers configuring document classification and extraction workflows using Studio activities, taxonomy management, digitization, and validation tools. It also includes the use of trained ML models and prebuilt extractors.
Topic 9	Email Automation: This section of the exam measures skills of RPA developers and covers automating email processes using Microsoft 365 and Gmail integrations. It focuses on sending, receiving, and managing emails as part of workflow automation.
Topic 10	UiPath Document Understanding: This section of the exam measures skills of RPA developers and covers the concepts and capabilities of UiPath Document Understanding, including processing various document types, understanding rule-based and ML-based extraction, and distinguishing DU from traditional OCR.
Topic 11	Platform Knowledge: This section of the exam measures skills of RPA developers and covers the high-level purpose and use of UiPath platform components, including Studio, Robots, Orchestrator, and Integration Service. It also explains the difference between attended and unattended processes, providing foundational knowledge of process deployment environments.
Topic 12	UiPath AI Center: This section of the exam measures skills of automation analysts and covers the basics of UiPath AI Center, its role in applying machine learning to automation, and the industries where AI models can be applied effectively.
Topic 13	Environments, Applications, and or Tools: This section of the exam measures skills of RPA developers and covers the candidate’s comfort level with common development tools, platforms, and environments such as Excel, Outlook, browsers, version control, Studio, Document Understanding Template, AI Center, and Communication Mining.
Topic 14	Exception Handling: This section of the exam measures skills of RPA developers and covers structured error handling using Try Catch, Throw, Rethrow, and Retry Scope. It prepares the candidate to handle and resolve automation errors gracefully.
Topic 15	Business Knowledge: This section of the exam measures skills of automation analysts and covers the fundamental understanding of business process automation, its value in real-world operations, and essential concepts used to identify, map, and analyze business processes.

UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q170-Q175):

NEW QUESTION # 170
How should the data be managed after extraction in the UiPath Document Understanding process?

A. Keep the data only in the queue items without serialization.
B. Directly pass the extracted data to other processes via Arguments.
C. Store the data temporarily in local folders before processing.
D. Serialize the data and store it in a storage bucket or similar location.

Answer: D

Explanation:
After the data is extracted using the UiPath Document Understanding framework, it is crucial to handle the data securely and efficiently for further use in other processes or storage. Serialization is often used to convert the extracted data into a format suitable for storage or transmission. By serializing the data, it can be stored in storage buckets (such as cloud storage or a database) and accessed later as needed for subsequent processing.
This approach ensures that data is preserved, secure, and available for other workflows or automations.
(Source: UiPath Document Understanding Documentation).

NEW QUESTION # 171
What are the out-of-the-box model types available in AI Center?

A. Pre-trained, custom training, and reviewed.
B. Pre-trained, custom training, and fine-tunable.
C. Custom training, fine-tunable, and reviewed.
D. Pre-trained, fine-tunable, and reviewed.

Answer: B

Explanation:
According to the UiPath documentation, AI Center provides three types of out-of-the-box model types that can be used for different purposes and scenarios1:
Pre-trained: These are models that are already trained on a large and diverse dataset and can be used as-is for inference or prediction. They do not require any additional data or training from the user. Examples of pre- trained models are Language Detection, Sentiment Analysis, and Question Answering.
Custom training: These are models that allow the user to train them on their own data using the AI Center UI or API. They require the user to upload a dataset, configure the training parameters, and monitor the training progress and results. Examples of custom training models are Image Classification, Text Classification, and Named Entity Recognition.
Fine-tunable: These are models that are pre-trained on a general dataset but can be further trained or fine- tuned on a specific dataset provided by the user. They offer the best of both worlds: the benefit of using a pre- trained model and the flexibility of customizing it to the user's needs. Examples of fine-tunable models are Object Detection, Text Summarization, and Language Translation.
References:
1: Out-of-the-Box Packages

NEW QUESTION # 172
Which of the following consumes Page Units?

A. Using Intelligent Form Extractor on a 5-page document with 0 successful extractions.
B. Using ML Classifier on a 21-page document.
C. Creation of a Document Validation Action in Action Center.
D. Applying OCR on a 10-page document.

Answer: D

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.
References:
AI Center - AI Units
Document Understanding - Metering & Charging Logic

NEW QUESTION # 173
Under what condition can a dataset be edited in UiPath AI Center?

A. If it has not been modified within the last 24 hours.
B. There are no restrictions in editing a dataset.
C. If it is not being used in any active pipeline.
D. If it is not linked to any data labeling session.

Answer: C

Explanation:
According to the UiPath documentation, a dataset is a folder of storage containing arbitrary sub-folders and files that allows machine learning models in your project to access new data points. You can edit a dataset's name, description, or content from the Datasets > [Dataset Name] page, by clicking Edit dataset. However, you can only edit a dataset if it is not currently being used in an active pipeline. A pipeline is a sequence of steps that defines how to train, test, and deploy a machine learning model. If a dataset is being used in an active pipeline, you will see a lock icon next to it, indicating that it cannot be edited. You can either wait for the pipeline to finish or stop it before editing the dataset.
References:
AI Center - Managing Datasets
AI Center - About Datasets
AI Center - About Pipelines

NEW QUESTION # 174
What is the correct order of uploading a package exported from UiPath AI Center?
Instructions: Drag the steps found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

Explanation:
A screenshot of a computer AI-generated content may be incorrect.

Export the package from AI Center. This is the first step where you prepare the package to be moved.
On the ML Packages page, click the Import ML Package button. This step is where you start the process of importing the package you've exported.
On the Upload package field, add the zip file downloaded using the Downloading ML Packages procedure.
After starting the import process, you will upload the actual package.
Click Create. This is the final step where you finalize the uploading process of your ML package.
Please proceed with these steps in the UiPath AI Center to upload your exported package correctly.

NEW QUESTION # 175
......

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