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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic	Details
Topic 1	Working with Files and Folders: This section of the exam measures skills of automation analysts and covers creating and managing files and folders within local directories, including iteration and file manipulation using Studio activities.
Topic 2	UiPath Communications Mining - Taxonomy Design: This section of the exam measures skills of RPA developers and covers how to design a taxonomy for Communications Mining, enabling models to interpret and structure data effectively during classification and automation processes.
Topic 3	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 4	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 5	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 6	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 7	Orchestrator: This section of the exam measures skills of RPA developers and covers Orchestrator's structure and functionality, including entities at the tenant and folder level. It includes using assets, queues, storage buckets, and provisioning robots along with setting up roles and logging.
Topic 8	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 9	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 10	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 11	Data Manipulation: This section of the exam measures skills of RPA developers and covers data handling with VB.Net string functions, RegEx patterns, arrays, lists, and dictionaries. It also covers DataTable operations such as building, filtering, and converting data for automation.
Topic 12	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 13	Integration Service: This section of the exam measures skills of automation analysts and covers the use of UiPath Integration Service, its connectors, and triggers, showing how these elements enable smooth interaction between UiPath and third-party systems.

UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q10-Q15):

NEW QUESTION # 10
What is the benefit of making an ML Skill public?

A. It allows access from inside of the UiPath environment.
B. It provides additional security measures for the ML Skill.
C. It allows access from outside of the UiPath environment.
D. It enables automatic updates and enhancements to the ML Skill without user intervention.

Answer: C

Explanation:
Making an ML Skill public in UiPath enables it to be accessed externally from the UiPath ecosystem. This can be beneficial if the ML Skill needs to be utilized in external applications, systems, or services beyond UiPath's automation environment. Public access expands the usability of the skill, allowing integration with other systems while maintaining security through managed endpoints.

NEW QUESTION # 11
How do you use the Generative Classifier within UiPath Document Understanding Cloud APIs to classify a document as either an "Invoice" or a "Receipt"?

A. By sending a GET request to /api/framework/projects/{projectld}/classifiers/generative_classifier
/classification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and descriptions.
B. By sending a POST request to yapi/framework/projectsy{projectld}/ctassifiers/generative_classifier
/classification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and descriptions.
C. By sending a PUT request to /api/framework/proiects/{projectld}/classifiers/generative_classifier
/classification without any specific classification details in the body of the request.
D. By sending a POST request to /api/framework/projects/{projectld}/classifiers/generative_classifier
/dassification with a body containing prompts that specify the 'Invoice' and 'Receipt' names and a description for their fields.

Answer: B

Explanation:
In UiPath Document Understanding Cloud APIs, the Generative Classifier is used to classify documents by leveraging a generative AI model. To classify a document as either an "Invoice" or a "Receipt," a POST request must be sent to the /api/framework/projects/{projectId}/classifiers/generative_classifier/classification endpoint. The body of this request should contain prompts specifying the classification types (in this case,
"Invoice" and "Receipt") along with their corresponding descriptions. This allows the model to correctly classify incoming documents based on these predefined prompts.
For further reading, refer to:
UiPath Document Understanding API Documentation: Cloud API for Classification Generative Classifier APIs: UiPath Classification APIs

NEW QUESTION # 12
What does the Train stage of the Document Understanding Framework do?

A. Allows the extractor to improve its prediction over time by using better OCR (Optical Character Recognition) engines.
B. Improves the extractor accuracy by learning from the classification result.
C. Allows the model to learn from human-validated data.
D. Allows a human to validate and correct the extracted data.

Answer: C

Explanation:
In the UiPath Document Understanding Framework, the Train stage enables models to learn from human- validated data. This process involves feeding the corrections made by humans during the validation phase back into the model, allowing it to refine its predictions and improve accuracy over time.
UiPath Documentation
The training component is crucial for classifiers and extractors capable of learning from human feedback. By incorporating validated data, these components can adjust their algorithms to better handle similar documents in the future, enhancing the overall efficiency and effectiveness of the automation process.
Other options are incorrect because:
B: Allows the extractor to improve its prediction over time by using better OCR engines: While better OCR engines can enhance data extraction, this is not the function of the Train stage.
C: Allows a human to validate and correct the extracted data: This describes the Validation stage, not the Train stage.
D: Improves the extractor accuracy by learning from the classification result: Training focuses on learning from human-validated extraction results, not just classification outcomes.
Therefore, the primary purpose of the Train stage is to allow the model to learn from human-validated data, thereby improving its future performance.

NEW QUESTION # 13
What can be done in the Reports section of the dataset navigation bar in UiPath Communication Mining?

A. Train models using unsupervised learning.
B. Monitor model performance and receive recommendations.
C. Access detailed, queryable charts, statistics, and customizable dashboards.
D. View, save, and modify dataset model versions.

Answer: C

Explanation:
Reference: UiPath Communication Mining Reports

NEW QUESTION # 14
Which of the below is the correct definition of "recall" in UiPath Communications Mining?

A. For a given concept, what % of cases will the model detect.
B. For a given concept what % of cases will the model correctly predict.
C. For a given concept, what % of cases will the model not detect.
D. For a given concept what % of cases will the model incorrectly predict.

Answer: A

Explanation:
Recall is a metric that measures the proportion of all possible true positives that the model was able to identify for a given concept1. A true positive is a case where the model correctly predicts the presence of a concept in the data. Recall is calculated as the ratio of true positives to the sum of true positives and false negatives, where a false negative is a case where the model fails to predict the presence of a concept in the data. Recall can be interpreted as the sensitivity or completeness of the model for a given concept2. For example, if there are 100 verbatims that should have been labelled as 'Request for information', and the model detects 80 of them, then the recall for this concept is 80% (80 / (80 + 20)). A high recall means that the model is good at finding all the relevant cases for a concept, while a low recall means that the model misses many of them.
References: 1: Recall 2: Precision and Recall

NEW QUESTION # 15
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