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UiPath-SAIv1 Reliable Mock Test, UiPath-SAIv1 Latest Exam Test

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q24-Q29):

NEW QUESTION # 24
What is Document Understanding?

A. A solution for combining different approaches to extract information from workflows.
B. A solution for combining different approaches to extract entities from Word documents such as contracts and agreements.
C. A solution that offers the ability to digitize, extract, validate, and train data from documents.
D. A solution for the processing of Excel files for extracting data tables.

Answer: C

NEW QUESTION # 25
What are the available options for Scoring in Document Manager, that apply to string fields only?

A. Exact match and Native string search.
B. Exact match and Levenshtein.
C. Exact match and First span.
D. Exact match and Auto.

Answer: B

Explanation:
In UiPath Document Manager, for string fields, the available options for scoring include:
* Exact match: This checks whether the extracted string exactly matches the expected value.
* Levenshtein: This method computes the "distance" between two strings, which is the number of single- character edits (insertions, deletions, or substitutions) required to change one string into the other. It is used to assess the similarity between the extracted string and the expected value.
These scoring methods help evaluate the accuracy of string extraction in the document understanding process.
For more details, refer to:
* UiPath Document Understanding Framework: String Field Scoring
* Levenshtein Distance in UiPath: Scoring String Fields

NEW QUESTION # 26
When dealing with variable-length data, or data spanning over multiple pages of the document (e.g. item tables), what is the recommended data extraction methodology to be used?

A. Hybrid data extraction.
B. Rule-based data extraction.
C. Model-based data extraction.
D. Manual data extraction.

Answer: C

Explanation:
Model-based data extraction, often involving machine learning models, is particularly effective for handling complex data structures such as variable-length data or data that spans multiple pages. This approach adapts better to varying formats and can extract information more accurately in such scenarios compared to rule- based or manual methods.

NEW QUESTION # 27
Which filter option should be used for the For Each File in Folder activity to iterate through all the Microsoft Word documents in a local folder?

A. "* doc, *docx"
B. Microsoft Word
C. "* doc"
D. ".doc"

Answer: D

Explanation:
To iterate through all Microsoft Word documents in a local folder using the For Each File in Folder activity, you should use the filter option "*.doc*". This filter utilizes the asterisk (*) wildcard to match any number of characters, effectively capturing both .doc and .docx file extensions.
UiPath Documentation
In the Filter by field of the activity, entering "*.doc*" ensures that the iteration includes all files with names ending in .doc or .docx, which are standard extensions for Microsoft Word documents.
Other options are incorrect because:
* B. Microsoft Word: This is not a valid filter pattern.
* *C. " doc"**: This pattern looks for files with names ending in " doc" and does not account for file extensions.
* *D. " doc, *docx"**: This is not a valid filter pattern; multiple patterns should be separated by semicolons (e.g., "*.doc;*.docx").
Therefore, the correct filter to use is "*.doc*".

NEW QUESTION # 28
What are the two main data extraction methodologies used in document understanding processes?

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

Answer: D

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 # 29
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