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UiPath UiPath-SAIAv1 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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 4

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 5

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.

Topic 6

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 7

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 8

Logging: This section of the exam measures skills of automation analysts and covers interpretation of robot execution logs and the application of logging best practices to support auditability, diagnostics, and monitoring.

Topic 9

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 10

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 11

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 12

Control Flow: This section of the exam measures skills of RPA developers and covers debugging methods and logic handling in projects. It introduces the use of breakpoints, tracepoints, and debugging panels for managing and improving workflow execution.

Topic 13

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 14

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 15

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.

UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q158-Q163):

NEW QUESTION # 158
What does the Export stage of the Document Understanding Framework do?

A. Extracts the text out of the image document using OCR (Optical Character Recognition).
B. Allows a human to validate and correct the extracted data.
C. Classifies the document as one of the predefined document types.
D. Converts the result of extraction to a dataset or to a customized format.

Answer: D

Explanation:
According to the UiPath documentation portal1, the Export stage of the Document Understanding Framework is the final stage of the document processing workflow, where the extracted data is converted to a dataset or to a customized format, such as Excel, JSON, or XML. The Export stage enables you to easily export data for training ML models, using the Export files dialog box in Data Manager or Document Manager. The Export stage also allows you to export the schema of the fields and their configurations, which can be imported into a different session. The Export stage supports different export options, such as current search results, all labeled, schema, or all. The Export stage also provides validation rules, such as requiring at least 10 labeled documents or pages for each field, and at least one document for each classification option1. Therefore, option A is the correct answer, as it describes the main function and benefit of the Export stage. Option B is incorrect, as it refers to the Data Validation stage, which is the previous stage of the document processing workflow, where a human can review and correct the extracted data using the Validation Station or the Present Validation Station activities2. Option C is incorrect, as it refers to the Classification stage, which is the second stage of the document processing workflow, where the document is classified as one of the predefined document types using the Classify Document Scope activity and a classifier of choice3. Option D is incorrect, as it refers to the Digitization stage, which is the first stage of the document processing workflow, where the text is extracted out of the image document using OCR (Optical Character Recognition) using the Digitize Document activity and an OCR engine of choice.
References: 1 Document Understanding - Export Documents 2 Document Understanding - Data Validation 3 Document Understanding - Classification Document Understanding - Digitization

NEW QUESTION # 159
The "Train" stage from Document Understanding Framework usually comes after?

A. Validation.
B. Digitization.
C. Extraction.
D. Classification.

Answer: A

Explanation:
Reference: UiPath Document Understanding Workflow

NEW QUESTION # 160
Under what condition can a project be deleted in UiPath AI Center?

A. If it does not have any deployed packages.
B. If it does not have any scheduled pipelines.
C. If it does not have any running pipelines.
D. If it does not have any pipeline data.

Answer: A

Explanation:
A project in UiPath AI Center is an isolated group of resources (datasets, pipelines, packages, skills, and logs) that you use to build a specific ML solution. You can create, edit, or delete projects from the Projects page or the project's Dashboard page. However, you can only delete a project if it does not have any package currently deployed in a skill. A package is a versioned and deployable unit of an ML model or an OS script that can be used to create an ML skill. A skill is a consumer-ready, live deployment of a package that can be used in RPA workflows in Studio. If a project has a package deployed in a skill, you need to undeploy the skill first before deleting the project. This is to ensure that you do not accidentally delete a project that is being used by a skill12.
References: 1: AI Center - Managing Projects 2: AI Center - Managing ML Skills

NEW QUESTION # 161
Which of the following statements is correct in the context of migrating a schema from Document Manager to a Modern Project?

A. If you import a schema into a document type that already contains a schema, the import will fail only if documents were already uploaded.
B. If you import a schema into a document type that already contains a schema, the import will be successful and the schema will be overridden.
C. If you import a schema into a document type that already contains a schema, the import will fail.
D. If you import a schema into a document type that already contains a schema, the import will be successful and the schema will be updated with the missing field.

Answer: B

Explanation:
When migrating a schema from Document Manager to a Modern Project, UiPath will override any existing schema associated with the document type if you attempt to import a new schema. This behavior is confirmed in UiPath's handling of document types and schema imports, where the new schema will replace the old one, ensuring that the latest version is applied to the project. This helps maintain consistency in schema configurations during migrations, especially when schemas are updated or need to be standardized

NEW QUESTION # 162
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 not detect.
B. For a given concept what % of cases will the model incorrectly predict.
C. For a given concept, what % of cases will the model detect.
D. For a given concept what % of cases will the model correctly predict.

Answer: C

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 # 163
......

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