UIPATH-SAIV1–100% FREE RELIABLE TEST MATERIALS | ACCURATE NEW UIPATH CERTIFIED PROFESSIONAL SPECIALIZED AI PROFESSIONAL V1.0 TEST COST

UiPath-SAIv1–100% Free Reliable Test Materials | Accurate New UiPath Certified Professional Specialized AI Professional v1.0 Test Cost

UiPath-SAIv1–100% Free Reliable Test Materials | Accurate New UiPath Certified Professional Specialized AI Professional v1.0 Test Cost

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q55-Q60):

NEW QUESTION # 55
How can you build custom models supported by AI Center?

  • A. Using the Al Center IDE (Integrated Development Environment).
  • B. Using the Al Center model builder.
  • C. Using a Python IDE (Integrated Development Environment) or an AutoML platform.
  • D. Using a C/C++ IDE (Integrated Development Environment), then upload the code to Al Center IDE.

Answer: C

Explanation:
To build custom models supported by AI Center, you can use a Python IDE or an AutoML platform of your choice. A Python IDE is a software application that provides tools and features for writing, editing, debugging, and running Python code. An AutoML platform is a service that automates the process of building and deploying machine learning models, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and model evaluation. Some examples of Python IDEs are PyCharm, Visual Studio Code, and Jupyter Notebook. Some examples of AutoML platforms are Google Cloud AutoML, Microsoft Azure Machine Learning, and DataRobot.
To use a Python IDE, you need to install the required Python packages and dependencies, write the code for your model, and test it locally. Then, you need to package your model as a zip file that follows the AI Center ML Package structure and requirements. You can then upload the zip file to AI Center and create an ML Skill to deploy and consume your model.
To use an AutoML platform, you need to sign up for the service, upload your data, configure your model settings, and train your model. Then, you need to export your model as a zip file that follows the AI Center ML Package structure and requirements. You can then upload the zip file to AI Center and create an ML Skill to deploy and consume your model.
References: AI Center - Building ML Packages, AI Center - ML Package Structure, AI Center - Creating ML Skills


NEW QUESTION # 56
Which activity should be used for classification validation in attended mode?

  • A. Wait for Document Classification and Resume.
  • B. Create Document Classification Action.
  • C. Train Classifiers Scope.
  • D. Present Classification Station.

Answer: D


NEW QUESTION # 57
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 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: A

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 # 58
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: D


NEW QUESTION # 59
What should a UiPath Communications Mining taxonomy contain when it is being imported?

  • A. Entity predictions.
  • B. Label descriptions.
  • C. Entity descriptions.
  • D. Label predictions.

Answer: B


NEW QUESTION # 60
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