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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q160-Q165):

NEW QUESTION # 160
What are the out-of-the-box model types available in AI Center?

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

Answer: D

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 # 161
Which of the following file types are supported for the DocumentPath property in the Classify Document Scope activity?

A. .bmp, .pdf, .jpe, .psd
B. .pdf, .jpeg, .raw, tif
C. .png, .gif, .jpe, .tiff
D. .jpe, .eps, .jpg, .tiff

Answer: C

Explanation:
According to the UiPath documentation portal1, the DocumentPath property in the Classify Document Scope activity accepts the path to the document you want to validate. This field supports only strings and String variables. The supported file types for this property field are .png, .gif, .jpe, .jpg, .jpeg, .tiff, .tif, .bmp, and .
pdf. Therefore, option B is the correct answer, as it contains four of the supported file types. Option A is incorrect, as .psd is not a supported file type. Option C is incorrect, as .raw is not a supported file type. Option D is incorrect, as .eps is not a supported file type.
References: 1 Activities - Classify Document Scope - UiPath Documentation Portal

NEW QUESTION # 162
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 session name and dataset.
D. Data labeling name, dataset. and Al Center project.

Answer: C

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 # 163
What is the correct execution order of the Document Understanding template stages?
Instructions: Drag the stages found on the "Left" and drop them on the "Right" in the correct order.

Answer:

Explanation:

Explanation:
The correct execution order of the Document Understanding template stages is:
Taxonomy
Digitize
Classify
Extract
Extraction Validation
Export
Comprehensive and Detailed Explanation: The Document Understanding template stages are based on a document processing flowchart that follows these steps:
First, you need to define the Taxonomy of the document types and fields that you want to process and extract information from. This is done using the Taxonomy Manager in UiPath Studio1.
Next, you need to Digitize the input documents, which can be in various formats such as PDF, image, or text. This is done using the Digitize Document activity, which converts the documents into a machine- readable format and performs OCR if needed2.
Then, you need to Classify the digitized documents into the predefined document types in your taxonomy. This is done using the Classify Document Scope activity, which can use various classifiers such as Keyword Based Classifier, Machine Learning Classifier, or Intelligent Form Extractor3.
After that, you need to Extract the relevant information from the classified documents based on the fields in your taxonomy. This is done using the Data Extraction Scope activity, which can use various extractors such as Regex Based Extractor, Machine Learning Extractor, or Form Extractor.
Next, you need to perform Extraction Validation to review and correct the extracted information, either manually or automatically. This is done using the Present Validation Station activity, which can use either the Validation Station or the Action Center for human-in-the-loop validation.
Finally, you need to Export the validated information to the desired output location, such as a file, a database, or a queue. This is done using the Export Extraction Results activity, which can use various exporters such as Excel Exporter, CSV Exporter, or Queue Item Exporter.
References:
UiPath Studio - Taxonomy Manager
UiPath Activities - Digitize Document
UiPath Activities - Classify Document Scope

NEW QUESTION # 164
Given the following scenario:
* You have a trained version of the Document Understanding Model with 1000 pages called v22.10.0.1.
* You have an evaluation dataset of 100 pages that gave a score of 0.72 for v22.10.0.1.
* The business team labeled 800 pages and they ask for an increment of the Model that would contain all
1000+800 pages.
What is the first recommended pipeline run configuration to create the new version?

A. Pipeline type: EvaluatePackage Major Version: 23.10.4.0 Package Minor Version: 0 Input Dataset:
1000+800 pages Evaluation Dataset: 100 pages
B. Pipeline type: Training Package Major Version: 23.10.4.0 Package Minor Version: 1 Input Dataset:
1000+800 Evaluation Dataset: N/A
C. Pipeline type: FullPackage Major Version: 23.10.4.0 Package Minor Version: 1 Input Dataset: 800 pages Evaluation Dataset: 100 pages
D. Pipeline type: Full Package Major Version: 23.10.4.0 Package Minor Version: 0 Input Dataset:
1000+800 pages Evaluation Dataset: 100 pages

Answer: D

Explanation:
The correct pipeline configuration for creating a new model version that includes both the initial 1000 pages and the newly labeled 800 pages is to run a Full pipeline. This ensures that the model is fully retrained with all available data (1800 pages in total). The evaluation dataset should remain the 100 pages to gauge the model's performance after retraining. This approach maximizes the model's learning by incorporating all labeled data while allowing for evaluation against a known dataset.

NEW QUESTION # 165
......

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