UIPATH-SAIV1 EXAM DUMPS & UIPATH-SAIV1 PREP4SURE TRAINING

UiPath-SAIv1 exam dumps & UiPath-SAIv1 prep4sure training

UiPath-SAIv1 exam dumps & UiPath-SAIv1 prep4sure training

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q193-Q198):

NEW QUESTION # 193
What are all the types of ML (Machine Learning) models supported by Al Center?

  • A. Out-of-the-box models from UiPath and UiPath technology partners.
  • B. Out-of-the-box models from UiPath, UiPath technology partners, open-source models from the community, and custom models.
  • C. Out-of-the-box models from UiPath technology partners, and open-source models from the community.
  • D. Out-of-the-box models from UiPath technology partners and custom models.

Answer: B

Explanation:
In UiPath AI Center, the platform supports several types of machine learning (ML) models, including:
* Out-of-the-box models from UiPath: Pre-built models designed for common automation tasks.
* Models from UiPath technology partners: External models developed by UiPath's partners.
* Open-source models: Community-contributed models that can be used and adapted for various use cases.
* Custom models: Models that users build and train specifically for their projects using their datasets.
This flexibility in model support ensures that organizations can leverage a wide range of machine learning capabilities to suit different automation needs.
For more details, refer to:
* UiPath AI Center Documentation: AI Center Models
* Machine Learning Model Types: Types of Models in UiPath AI Center


NEW QUESTION # 194
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?

  • A. Consider the document types, language, and data quality when choosing an ML Extractor.
    It is important to select one that is specifically trained or optimized for the document types being processed.
    It is also important to take into account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.
  • B. The size of the ML Extractor is the most important factor to consider when choosing an ML Extractor.Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.
  • C. The popularity of the ML Extractor among other UiPath users should be the primary factor when choosing a UiPath ML Extractor.
    Opt for the ML Extractor that has the highest number of downloads or positive reviews.
  • D. The cost of the ML Extractor should be the main consideration when choosing an ML Extractor.
    Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.

Answer: A

Explanation:
The ML Extractor is a data extraction tool that uses machine learning models provided by UiPath to identify and extract data from documents. The ML Extractor can work with predefined document types, such as invoices, receipts, purchase orders, and utility bills, or with custom document types that are trained using the Data Manager and the Machine Learning Classifier Trainer12.
According to the best practice, the ML Extractor should be chosen based on the document types, language, and data quality of the documents being processed. It is important to select an ML Extractor that is specifically trained or optimized for the document types that are relevant for the use case, as different document types may have different layouts, fields, and formats. It is also important to take into account the language of the documents, as some ML Extractors may support only certain languages or require specific language settings. Moreover, it is important to consider the quality and diversity of the training data used to train the ML Extractor, as this may affect the accuracy and reliability of the extraction results. The training data should be representative of the real-world data, and should cover various scenarios, variations, and exceptions3.
References: 1: Machine Learning Extractor - UiPath Activities 2: Machine Learning Classifier Trainer - UiPath Document Understanding 3: Data Extraction - UiPath Document Understanding


NEW QUESTION # 195
Which of the following extractors can be used for Data Extraction Scope activity?

  • A. Intelligent Form Extractor, Machine Learning Extractor. Logic Extractor, and Regex Based Extractor.
  • B. Full Extractor. Machine Learning Extractor, Intelligent Form Extractor, and Regex Based Extractor.
    C Form Extractor Incremental Extractor Machine Learning Extractor and Intelligent Form Extractor
  • C. Regex Based Extractor. Form Extractor. Intelligent Form Extractor, and Machine Learning Extractor.

Answer: C


NEW QUESTION # 196
Which is a high-level view of the tabs within an AI Center project?

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

Answer: B

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 # 197
Which of the following options contains the correct list of Default actions that can be found in Workflow Analyzer Settings?

  • A. Error, Info, Warning, Verbose
  • B. Fatal, Error, Warning, Info, Trace
  • C. Exception, Info, Warning, Verbose
  • D. Error, Critical, Info, Warning, Verbose

Answer: B


NEW QUESTION # 198
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