Practice Free UiPath-SAIv1 Exam Online Questions
Question #61
Which of the following OCR (Optical Character Recognition) engines is not free of charge?
- A . Tesseract.
- B . Microsoft Azure OCR.
- C . OmniPaqe.
- D . Microsoft OCR.
Correct Answer: C
C
Explanation:
According to the UiPath documentation, OmniPaqe is a paid OCR engine that requires a license to use. It is one of the most accurate and reliable OCR engines available, and it supports over 200 languages. The other OCR engines listed are free of charge, but they may have different features, limitations, and performance levels. For example, Tesseract is an open-source OCR engine that supports over 100 languages, but it may not be as accurate as OmniPaqe. Microsoft Azure OCR and Microsoft OCR are both cloud-based OCR engines that use Microsoft’s technology, but they have different capabilities and pricing models. Microsoft Azure OCR can process both printed and handwritten text, and it uses a pay-as-you-go model based on the number of transactions. Microsoft OCR can only process printed text, and it is included in the UiPath Studio license.
References:
Document Understanding – OCR Engines
Automation Pricing – Complete UiPath Enterprise Solution
C
Explanation:
According to the UiPath documentation, OmniPaqe is a paid OCR engine that requires a license to use. It is one of the most accurate and reliable OCR engines available, and it supports over 200 languages. The other OCR engines listed are free of charge, but they may have different features, limitations, and performance levels. For example, Tesseract is an open-source OCR engine that supports over 100 languages, but it may not be as accurate as OmniPaqe. Microsoft Azure OCR and Microsoft OCR are both cloud-based OCR engines that use Microsoft’s technology, but they have different capabilities and pricing models. Microsoft Azure OCR can process both printed and handwritten text, and it uses a pay-as-you-go model based on the number of transactions. Microsoft OCR can only process printed text, and it is included in the UiPath Studio license.
References:
Document Understanding – OCR Engines
Automation Pricing – Complete UiPath Enterprise Solution
Question #62
How is the Taxonomy component used in the Document Understanding Template?
- A . To define the document types and the pieces of information targeted for data extraction (fields) for each document type.
- B . To assign predefined document categories based on content similarity, simplifying classification tasks for easier document organization.
- C . To convert scanned documents into machine-readable formats, utilizing taxonomy rules to enhance digitized content accuracy.
- D . To automatically extract structured data fields from documents, leveraging taxonomy mappings for precise data extraction.
Correct Answer: A
A
Explanation:
The Taxonomy component is used to define document types, categories, and the fields to extract for each document type. This ensures a structured approach to document processing.
Reference: UiPath Taxonomy Manager
A
Explanation:
The Taxonomy component is used to define document types, categories, and the fields to extract for each document type. This ensures a structured approach to document processing.
Reference: UiPath Taxonomy Manager
Question #63
What is the primary metric used to calculate the score for the All Labels performance factor in UiPath Communications Mining?
- A . Mean Average Precision.
- B . Number of labels.
- C . Volume of data uploaded.
- D . Number of annotations.
Correct Answer: A
A
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)
A
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)
Question #64
Which validation checks are performed for ML packages uploaded with the Enable Training option inactive?
- A . Existence of a non-empty root folder, requirements.txt file, and train.py file in the root folder which implements a class Main. The class is further validated to implement an__init_function.
- B . Existence of a non-empty root folder, requlrements.txt file, and main.py file in the root folder which implements a class Main. The class is further validated to implement an__init and a predict function.
- C . Existence of a non-empty root folder, main.py file in the root folder which implements a dass Main. The class is further validated to implement an__init__and a predict function.
- D . Existence of a requirements.txt file, and main.py file which implements a class Main. The class is further validated lo implement an__init__and a predict function.
Correct Answer: B
B
Explanation:
When uploading an ML package in UiPath AI Center with the Enable Training option inactive, several validation checks are performed:
There must be a non-empty root folder.
A requirements.txt file must be present to define dependencies.
A main.py file should be in the root folder, which implements a class Main.
The class must implement the necessary methods, such as __init__ and predict.
For more details, refer to:
UiPath AI Center Documentation: ML Package Validation
B
Explanation:
When uploading an ML package in UiPath AI Center with the Enable Training option inactive, several validation checks are performed:
There must be a non-empty root folder.
A requirements.txt file must be present to define dependencies.
A main.py file should be in the root folder, which implements a class Main.
The class must implement the necessary methods, such as __init__ and predict.
For more details, refer to:
UiPath AI Center Documentation: ML Package Validation