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UiPath-SAIv1 exam braindumps: UiPath Certified Professional Specialized AI Professional v1.0 & UiPath-SAIv1 study guide

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q43-Q48):

NEW QUESTION # 43
Which is a high-level view of the tabs within an AI Center project?

A. Dashboard. Datasets. ML Packages. ML Training. ML Evaluation, and ML Logs.
B. Datasets, Data Labeling. ML Packages, ML Training, ML Evaluation, ML Skills, and ML Logs.
C. Dashboard. Datasets, Data Labeling. ML Packages. Pipelines, ML Skills, and ML Logs.
D. Datasets. Data Labeling. ML Packages. Pipelines, and ML Skills.

Answer: C

NEW QUESTION # 44
Which is a high-level view of the tabs within an AI Center project?

A. Dashboard. Datasets. ML Packages. ML Training. ML Evaluation, and ML Logs.
B. Datasets, Data Labeling. ML Packages, ML Training, ML Evaluation, ML Skills, and ML Logs.
C. Dashboard. Datasets, Data Labeling. ML Packages. Pipelines, ML Skills, and ML Logs.
D. Datasets. Data Labeling. ML Packages. Pipelines, and ML Skills.

Answer: C

Explanation:
A high-level view of the tabs within an AI Center project is as follows:
* Dashboard: This tab provides an overview of the project's status, such as the number of datasets, pipelines, packages, skills, and logs, as well as the AI Units consumption and quota.
* Datasets: This tab enables you to upload, view, and manage the datasets that are used for training and evaluating the ML models within the project. A dataset is a folder of storage containing arbitrary files and sub-folders1.
* Data Labeling: This tab enables you to upload raw data, annotate text data in the labeling tool (for classification or entity recognition), and use the labeled data to train ML models. It is also used by the human reviewer to re-label incorrect predictions as part of the feedback process2.
* ML Packages: This tab enables you to upload, view, and manage the ML packages and package versions within the project. An ML package is a group of package versions of the same package type, and a package version is a trained model that can be deployed to a skill3.
* Pipelines: This tab enables you to create, view, and manage the pipelines and pipeline runs within the project. A pipeline is a description of an ML workflow, including the functions and their order of execution, and a pipeline run is an execution of a pipeline based on code provided by the user4.
* ML Skills: This tab enables you to deploy, view, and manage the ML skills within the project. An ML skill is a live deployment of a package version, which can be consumed by an RPA workflow using an ML skill activity in UiPath Studio5.
* ML Logs: This tab enables you to view and filter the logs related to the project, such as the events, messages, and errors that occurred during the pipeline runs, skill deployments, and skill executions6.
References:
1: About Datasets 2: About Data Labeling 3: About ML Packages 4: About Pipelines 5: About ML Skills 6: About ML Logs

NEW QUESTION # 45
What information should be provided when adding a classification label for the OOB (Out Of the Box) labeling template?

A. Name, Input to be labeled. Attribute name, and Shortcut.
B. Name, Classification Type. Attribute name. Color, and Shortcut.
C. Name, Classification type. Input to be labeled. Attribute name. Shortcut, and Color.
D. Name, Classification type, Attribute name, and Shortcut.

Answer: C

Explanation:
When setting up a classification label in UiPath's Out Of the Box (OOB) labeling templates, you need to provide several key details: the name of the label, the classification type (which defines the kind of label), the input to be labeled, the attribute name that describes the label's context, a shortcut for quick access, and a color for visual distinction. These fields ensure the label is fully defined and easy to manage in workflows.
(Source: UiPath Document Understanding documentation)

NEW QUESTION # 46
What can be done in the Reports section of the dataset navigation bar in UiPath Communication Mining?

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

Answer: A

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 : [Communications Mining - Using Label Summary]
1: [Communications Mining - Using Trends] : [Communications Mining - Using Segments]
2:[Communications Mining - Using Threads] : [Communications Mining - Using Comparison]

NEW QUESTION # 47
A project contains a Try Catch activity in the "Main.xaml" workflow. In the Catches block, there is a Rethrow activity. The process is started from Orchestrator and an exception is caught in the Try section. What is the expected result?

A. Exception Pop-up is displayed on the robot machine.
B. Job is completed with a "Stopped" state.
C. Job is completed with a "Successful" state.
D. Job is completed with a "Faulted" state.

Answer: D

NEW QUESTION # 48
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