UIPATH-SAIV1 KNOWLEDGE POINTS - RELIABLE UIPATH-SAIV1 TEST BOOTCAMP

UiPath-SAIv1 Knowledge Points - Reliable UiPath-SAIv1 Test Bootcamp

UiPath-SAIv1 Knowledge Points - Reliable UiPath-SAIv1 Test Bootcamp

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q80-Q85):

NEW QUESTION # 80
Which of the following consumes Page Units?

  • A. Applying OCR on a 10-page document.
  • B. Using ML Classifier on a 21-page document.
  • C. Using Intelligent Form Extractor on a 5-page document with 0 successful extractions.
  • D. Creation of a Document Validation Action in Action Center.

Answer: A


NEW QUESTION # 81
What is the correct order of uploading a package exported from UiPath AI Center?
Instructions: Drag the steps found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

Explanation:

* Export the package from AI Center. This is the first step where you prepare the package to be moved.
* On the ML Packages page, click the Import ML Package button. This step is where you start the process of importing the package you've exported.
* On the Upload package field, add the zip file downloaded using the Downloading ML Packages procedure. After starting the import process, you will upload the actual package.
* Click Create. This is the final step where you finalize the uploading process of your ML package.
Please proceed with these steps in the UiPath AI Center to upload your exported package correctly.


NEW QUESTION # 82
Which environment variable is relevant for Evaluation pipelines?

  • A. eval.redo_ocr
  • B. eval.use_cuda
  • C. eval.enable_qpu
  • D. eval.enable_ocr

Answer: A

Explanation:
The environment variable eval.redo_ocr is relevant for Evaluation pipelines because it allows you to rerun OCR when running the pipeline to assess the impact of OCR on extraction accuracy. This assumes an OCR engine was configured when the ML Package was created. The other options are not valid environment variables for Evaluation pipelines.
References: Document Understanding - Evaluation Pipelines


NEW QUESTION # 83
What information is required when creating a data labeling session?

  • A. Data labeling name, language, and number of documents.
  • B. Dataset and Al Center project.
  • C. Data labeling name, dataset. and Al Center project.
  • D. Data labeling session name and dataset.

Answer: D

Explanation:
When creating a data labeling session in UiPath AI Center, the key pieces of information required are:
* The data labeling session name: A unique identifier for the session.
* The dataset: The data that will be used in the labeling session.
For more details, refer to:
* UiPath AI Center Documentation: Data Labeling Sessions


NEW QUESTION # 84
What are the two main data extraction methodologies used in document understanding processes?

  • A. Rule-based and model-based data extraction.
  • B. Manual and model-based data extraction.
  • C. Hybrid and manual data extraction.
  • D. Rule-based and hybrid data extraction.

Answer: A

Explanation:
According to the UiPath documentation, there are two common types of data extraction methodologies used in document understanding processes: rule-based data extraction and model-based data extraction12. Rule- based data extraction targets structured documents, such as forms, invoices, or receipts, that have a fixed layout and a predefined set of fields. Rule-based data extraction uses predefined rules, such as regular expressions, keywords, or coordinates, to locate and extract the relevant data from the documents1. Model- based data extraction is used to process semi-structured and unstructured documents, such as contracts, emails, or reports, that have a variable layout and a diverse set of fields. Model-based data extraction uses machine learning models, such as neural networks, to learn from examples and extract the relevant data from the documents1. Both methodologies have their advantages and limitations, and depending on the use case, they can be used separately or in combination, in a hybrid approach2.
References: 1: Data Extraction Overview 2: Document Processing with Improved Data Extraction


NEW QUESTION # 85
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