100% PASS UIPATH-SAIAV1 - ACCURATE UIPATH SPECIALIZED AI ASSOCIATE EXAM (2023.10) FREE PRACTICE EXAMS

100% Pass UiPath-SAIAv1 - Accurate UiPath Specialized AI Associate Exam (2023.10) Free Practice Exams

100% Pass UiPath-SAIAv1 - Accurate UiPath Specialized AI Associate Exam (2023.10) Free Practice Exams

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q80-Q85):

NEW QUESTION # 80
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

Explanation:
Reference: UiPath Workflow Analyzer


NEW QUESTION # 81
What are the out-of-the-box model types available in AI Center?

  • A. Pre-trained, custom training, and fine-tunable.
  • B. Pre-trained, fine-tunable, and reviewed.
  • C. Custom training, fine-tunable, and reviewed.
  • D. Pre-trained, custom training, and reviewed.

Answer: A

Explanation:
According to the UiPath documentation, AI Center provides three types of out-of-the-box model types that can be used for different purposes and scenarios1:
Pre-trained: These are models that are already trained on a large and diverse dataset and can be used as-is for inference or prediction. They do not require any additional data or training from the user. Examples of pre- trained models are Language Detection, Sentiment Analysis, and Question Answering.
Custom training: These are models that allow the user to train them on their own data using the AI Center UI or API. They require the user to upload a dataset, configure the training parameters, and monitor the training progress and results. Examples of custom training models are Image Classification, Text Classification, and Named Entity Recognition.
Fine-tunable: These are models that are pre-trained on a general dataset but can be further trained or fine- tuned on a specific dataset provided by the user. They offer the best of both worlds: the benefit of using a pre- trained model and the flexibility of customizing it to the user's needs. Examples of fine-tunable models are Object Detection, Text Summarization, and Language Translation.
References:
1: Out-of-the-Box Packages


NEW QUESTION # 82
On at least how many different pages should a regular field be labeled in Data Manager before Exporting the labeled documents to Al Center?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: C

Explanation:
To ensure the model is trained effectively, UiPath recommends that regular fields be labeled on at least 10 different pages in Data Manager before exporting the labeled documents to AI Center. This helps in providing enough variation and examples for the model to learn from and generalize effectively.


NEW QUESTION # 83
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. Input to be labeled. Attribute name. Shortcut, and Color.
  • C. Name, Classification Type. Attribute name. Color, and Shortcut.
  • D. Name, Classification type, Attribute name, and Shortcut.

Answer: B

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 # 84
What is the primary metric used to calculate the score for the All Labels performance factor in UiPath Communications Mining?

  • A. Number of annotations.
  • B. Volume of data uploaded.
  • C. Number of labels.
  • D. Mean Average Precision.

Answer: D

Explanation:
The Mean Average Precision (MAP) is the primary metric used in UiPath Communications Mining to assess the performance of classification models, specifically for the "All Labels" factor. MAP evaluates how well the model predicts labels by considering both the precision and recall of the predictions. This metric is commonly used in information retrieval systems to provide an overall measure of accuracy across all labels in the dataset.
(Source: UiPath Communications Mining documentation)


NEW QUESTION # 85
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