UIPATH-SAIV1 VALID TEST PAPERS & REAL UIPATH-SAIV1 TORRENT

UiPath-SAIv1 Valid Test Papers & Real UiPath-SAIv1 Torrent

UiPath-SAIv1 Valid Test Papers & Real UiPath-SAIv1 Torrent

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q137-Q142):

NEW QUESTION # 137
What information is required when creating a data labeling session?

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

Answer: A

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 # 138
When creating a training dataset, what is the recommended number of samples for the Classification fields?

  • A. 5-10 document samples from each class.
  • B. 20-50 document samples from each class.
  • C. 50-200 document samples from each class.
  • D. 10-20 document samples from each class.

Answer: B


NEW QUESTION # 139
What is the difference between OCR (Optical Character Recognition) and IntelligentOCR?

  • A. OCR (Optical Character Recognition) is a method that reads text from images, recognizing each character and its position, while IntelligentOCR is an enhanced version of it that can also work with noisier input data.
  • B. IntelligentOCR is simply a rebranding of the OCR (Optical Character Recognition), both of them being methods that read text from images, recognizing each character and its position.
  • C. IntelligentOCR is a UiPath Studio activity package that contains all the activities needed to enable information extraction, while OCR (Optical Character Recognition) is a method that reads text from images, recognizing each character and its position.
  • D. OCR (Optical Character Recognition) is a UiPath Studio activity package that contains IntelligentOCR as an activity used to read text from images, recognizing each character and its position. OCR is widely used in Document Understanding processes.

Answer: C

Explanation:
According to the UiPath documentation and web search results, OCR (Optical Character Recognition) is a method that reads text from images, recognizing each character and its position. OCR is used to digitize documents and make them searchable and editable. OCR can be performed by different engines, such as Tesseract, Microsoft OCR, Microsoft Azure OCR, OmniPaqe, and Abbyy. OCR is a basic step in the Document Understanding Framework, which is a set of activities and services that enable the automation of document processing workflows.
IntelligentOCR is a UiPath Studio activity package that contains all the activities needed to enable information extraction from documents. Information extraction is the process of identifying and extracting relevant data from documents, such as fields, tables, entities, and labels. IntelligentOCR uses different components, such as classifiers, extractors, validators, and trainers, to perform information extraction.
IntelligentOCR also supports different formats, such as PDF, PNG, JPG, TIFF, and BMP. IntelligentOCR is an advanced step in the Document Understanding Framework, which builds on the OCR output and provides more functionality and flexibility.
References:
* About the IntelligentOCR Activities Package
* OCR Activities
* OCR Feature Comparison: Uipath Community vs Uipath Licensed OCR
* Document Understanding - Introduction


NEW QUESTION # 140
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:


* 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 # 141
What is the default visibility of an ML skill?

  • A. An ML skill is by default public and can't be made private.
  • B. An ML skill is by default private and can't be made public.
  • C. An ML skill is by default private and can be made public.
  • D. An ML skill is by default public and can be made private.

Answer: C


NEW QUESTION # 142
......

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