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Validate Your Skills with UiPath UiPath-SAIAv1 Exam Dumps
By evaluating your shortcomings, you can gradually improve without losing anything in the UiPath Specialized AI Associate Exam (2023.10) (UiPath-SAIAv1) exam. You can take our customizable UiPath-SAIAv1 practice test multiple times, and as a result, you will get better results each time you progress and cover the topics of the real UiPath-SAIAv1 test. The software is compatible with Windows so you can run it easily on your computer.
UiPath UiPath-SAIAv1 Exam Syllabus Topics:
Topic
Details
Topic 1
- 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 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
- Variables and Arguments: This section of the exam measures skills of automation analysts and covers the creation and management of variables and arguments. It introduces key data types and explains how to apply variables and arguments across workflows to pass, store, and manipulate data.
Topic 4
- 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 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
- 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 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
- Studio Interface: This section of the exam measures skills of RPA developers and covers essential navigation and setup within UiPath Studio. It includes installing Studio, connecting to Orchestrator, navigating the interface, managing packages, configuring activity settings, and publishing processes to Orchestrator.
Topic 9
- 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 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
- 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 12
- 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 13
- 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.
Topic 14
- 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 15
- 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 16
- 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 17
- 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.
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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q220-Q225):
NEW QUESTION # 220
What is the correct order of recommended steps when introducing new labels into a mature taxonomy?
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.
Create the new label by assigning it at least once. This is the initial step to introduce a new category or classification within your data taxonomy.
Search for instances in the reviewed data where the new label should have been assigned, and apply the label accordingly. This step is crucial for maintaining consistency across your data set.
Use 'Missed Label' to find all additional missing examples in the reviewed data. This action helps in identifying and rectifying any instances that may have been overlooked during the initial review.
Check validation to ensure the label is performing as expected, and follow recommended actions if further training is required. Validation is key to assess the accuracy and performance of the new label within the system.
NEW QUESTION # 221
What does the following expression do?
subTotalAdditions.Select(Function(field) CDec(documentFields(field))).ToList.Sum() + subtotal
- A. Sums up all the line amounts converted to CDec and the subtotal
- B. Sums up all the line amounts and converts the fields to CDec.
- C. Sums up the subtotal to the total variable by converting it to CDec
- D. Sums up subtotal fields from the config file converted to CDec with the subtotal.
Answer: A
Explanation:
The expression does the following:
It uses the subTotalAdditions variable, which is a list of field names that represent the line amounts in the document.
It uses the Select method to apply a function to each element of the list. The function takes a field name as an argument and returns the value of the corresponding document field converted to a decimal number using the CDec function.
It uses the ToList method to convert the result of the Select method into a list of decimal numbers.
It uses the Sum method to calculate the sum of the elements in the list.
It adds the subtotal variable, which is another decimal number, to the sum.
The expression returns the total amount of the document, which is the sum of all the line amounts and the subtotal.
References:
VB.NET - Select Method
VB.NET - CDec Function
VB.NET - ToList Method
[VB.NET - Sum Method]
NEW QUESTION # 222
What are the mandatory activities to be included in an automation workflow to allow a remote knowledge worker to pick up an action that validates the extracted data in the form of a Document Validation Action?
- A. Present Validation Station, Wait for Document Validation Action and Resume.
- B. Orchestration Process Activities.
- C. Document Understanding Process Activities.
- D. Create Document Validation Action, Wait for Document Validation Action and Resume.
Answer: D
Explanation:
To enable a remote knowledge worker to validate the extracted data from documents in Action Center, the automation workflow needs to include the following activities12:
Create Document Validation Action: This activity creates an action of type Document Validation in Orchestrator Action Center, and returns an action object as output. The action object contains the information needed to resume the workflow after the human validation is completed. The input properties of this activity include the action details, such as title, priority, catalog, and folder, and the document validation data, such as the document object model, the document text, the taxonomy, and the automatic extraction results.
Wait for Document Validation Action and Resume: This activity suspends the execution of the workflow until the human validation is done in Action Center, and then resumes it with the updated extraction results. The input property of this activity is the action object obtained from the Create Document Validation Action activity. The output property is the validated extraction results, which can be used for further processing or exporting.
References: 1: Create Document Validation Action 2: Wait for Document Validation Action and Resume
NEW QUESTION # 223
How does Al Center handle versioning of ML packages?
- A. Only ML packages retrained via pipelines change their minor version.
- B. Versioning is not supported for ML packages.
- C. Only ML packages retrained via pipelines change their major version.
- D. Only ML packages uploaded by users change their minor version.
Answer: A
Explanation:
In UiPath AI Center, versioning of ML packages is an integral part of managing the lifecycle of machine learning models. Each ML package has a major and minor version to track changes. When an ML package is initially uploaded, it is assigned a major version (e.g., 1.0). If a package is retrained via pipelines, it increments the minor version (e.g., from 1.0 to 1.1). This mechanism helps differentiate between user- uploaded packages and those that have been retrained. Only packages that undergo retraining through pipelines modify their minor version, not the major version. Thus, answer D is correct.
(Source: docs.uipath.com on ML Package Management).
NEW QUESTION # 224
What can be done in the Reports section of the dataset navigation bar in UiPath Communication Mining?
- A. View, save, and modify dataset model versions.
- B. Train models using unsupervised learning.
- C. Access detailed, quervable charts, statistics, and customizable dashboards.
- D. Monitor model performance and receive recommendations.
Answer: C
Explanation:
The Reports section of the dataset navigation bar in UiPath Communication Mining allows users to access detailed, quervable charts, statistics, and customizable dashboards that provide valuable insights and analysis on their communications data1. The Reports section has up to six tabs,depending on the data type, each designed to address different reporting needs2:
Dashboard: Users can create custom dashboard views using data from other tabs, such as label summary, trends, segments, threads, and comparison. Dashboards are specific to the dataset and can be edited, deleted, or renamed by users with the 'Modify datasets' permission3.
Label Summary: Users can view high-level summary statistics for labels, such as volume, precision, recall, and sentiment. Users can also filter by data type, source, date range, and label category.
Trends: Users can view charts for verbatim volume, label volume, and sentiment over a selected time period.
Users can also filter by data type, source, date range, and label category.
Segments: Users can view charts comparing label volumes to verbatim metadata fields, such as sender domain, channel, or language. Users can also filter by data type, source, date range, and label category.
Threads: Users can view charts of thread volumes and label volumes within a thread, if the data is in thread form, such as call transcripts or email chains. Users can also filter by data type, source, date range, and label category.
Comparison: Users can compare different cohorts of data against each other, such as different sources, time periods, or label categories. Users can also filter by data type, source, date range, and label category.
References: 1: Communications Mining - Using Reports 2: Communications Mining - Reports 3: Communications Mining - Using Dashboards 4: [Communications Mining - Using Label Summary]
5: [Communications Mining - Using Trends] 6: [Communications Mining - Using Segments] 7:[Communications Mining - Using Threads] 8: [Communications Mining - Using Comparison]
NEW QUESTION # 225
......
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