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[General] Exam UiPath UiPath-SAIAv1 Vce Format, Latest UiPath-SAIAv1 Exam Labs

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【General】 Exam UiPath UiPath-SAIAv1 Vce Format, Latest UiPath-SAIAv1 Exam Labs

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q170-Q175):NEW QUESTION # 170
Which environment variable is relevant for Evaluation pipelines?
  • A. eval.enable_ocr
  • B. eval.use_cuda
  • C. eval.redo_ocr
  • D. eval.enable_qpu
Answer: C
Explanation:
The environment variable eval.redo_ocr is relevant for Evaluation pipelines because it allows you to rerun OCR when running the pipeline to assess the impact of OCR on extraction accuracy. This assumes an OCR engine was configured when the ML Package was created. The other options are not valid environment variables for Evaluation pipelines.
References: Document Understanding - Evaluation Pipelines

NEW QUESTION # 171
How do you load a taxonomy from a given non-default location text file into a variable?
Instructions: Drag the steps found on the "Left" and drop them on the "Right" in the correct order.

Answer:
Explanation:

Explanation:
A close-up of a page AI-generated content may be incorrect.

to load a taxonomy from a given non-default location text file into a variable, the order of steps should be as follows:
Create a new variable of type 'DocumentTaxonomy' called 'documentTaxonomy', then create a new string variable called 'taxonomyPath'.
This step involves setting up the necessary variables that will be used in the process. The
'documentTaxonomy' variable will hold the deserialized taxonomy object, and 'taxonomyPath' will store the path to the taxonomy file.
Using an Assign activity, assign the given non-default taxonomy text file path to the most recent variable created.
Here you will assign the path of the taxonomy file to the 'taxonomyPath' variable.
Use the Read Text File activity on the 'taxonomyPath' variable created. Next, create a new variable
'taxonomyText', then save the output of the Read Text File activity to 'taxonomyText'.
This step is where you read the contents of the taxonomy file using the 'Read Text File' activity. The contents are stored in the 'taxonomyText' variable.
Inside the last Assign activity 'Value to Save' property add the following expression: "Newtonsoft.Json.
JsonConvert.DeserializeObject(Of DocumentTaxonomy)(taxonomyText)" where 'taxonomyText' is the text read from the file and 'documentTaxonomy' (the most recent variable created) is the variable you created.
In this final step, you will deserialize the JSON content from the 'taxonomyText' into a 'DocumentTaxonomy' object using the 'JsonConvert.DeserializeObject' method and assign it to the 'documentTaxonomy' variable.
Following these steps in this order will load the taxonomy from a text file into the 'documentTaxonomy' variable in UiPath.

NEW QUESTION # 172
Which of the following statements is true regarding reviewing and applying entities in UiPath Communications Mining?
  • A. All of the entities within a paragraph should be reviewed.
  • B. If the entity value is correctly predicted, but the entity type is wrong, it cannot be changed.
  • C. All of the entities in a communication must be reviewed.
  • D. A single entity value can be split across multiple paragraphs.
Answer: A
Explanation:
According to the UiPath Communications Mining documentation, reviewing and applying entities is a crucial step for improving the accuracy and performance of the entity extraction models. When reviewing entities, users should check all of the predicted entities within a paragraph, as well as any missing or incorrect ones.
Users can accept, reject, edit, or create entities using the platform's interface or keyboard shortcuts. Users can also change the entity type if the value is correct but the type is wrong. Reviewing and applying entities helps the platform learn from the user feedback and refine its predictions over time. It also helps users assess the automation potential and benefit of the communications data.
References:
Communications Mining - Reviewing and applying entities
Communications Mining - Improving entity performance

NEW QUESTION # 173
Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?
  • A. The popularity of the ML Extractor among other UiPath users should be the primary factor when choosing a UiPath ML Extractor.Opt for the ML Extractor that has the highest number of downloads or positive reviews.
  • B. The size of the ML Extractor is the most important factor to consider when choosing an ML Extractor.Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.
  • C. The cost of the ML Extractor should be the main consideration when choosing an ML Extractor.Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.
  • D. Consider the document types, language, and data quality when choosing an ML Extractor.It is important to select one that is specifically trained or optimized for the document types being processed.
    It is also important to take into account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.
Answer: D
Explanation:
The ML Extractor is a data extraction tool that uses machine learning models provided by UiPath to identify and extract data from documents. The ML Extractor can work with predefined document types, such as invoices, receipts, purchase orders, and utility bills, or with custom document types that are trained using the Data Manager and the Machine Learning Classifier Trainer12.
According to the best practice, the ML Extractor should be chosen based on the document types, language, and data quality of the documents being processed. It is important to select an ML Extractor that is specifically trained or optimized for the document types that are relevant for the use case, as different document types may have different layouts, fields, and formats. It is also important to take into account the language of the documents, as some ML Extractors may support only certain languages or require specific language settings. Moreover, it is important to consider the quality and diversity of the training data used to train the ML Extractor, as this may affect the accuracy and reliability of the extraction results. The training data should be representative of the real-world data, and should cover various scenarios, variations, and exceptions3.
References: 1: Machine Learning Extractor - UiPath Activities 2: Machine Learning Classifier Trainer - UiPath Document Understanding 3: Data Extraction - UiPath Document Understanding

NEW QUESTION # 174
Which role is responsible for building and uploading ML (Machine Learning) models lo Al Center?
  • A. Administrator.
  • B. Process Controller.
  • C. RPA Developer.
  • D. Data Scientist.
Answer: D
Explanation:
In UiPath, the Data Scientist role is primarily responsible for creating, training, and uploading machine learning models to AI Center. While RPA developers might integrate these models into workflows, it is the Data Scientist who builds and fine-tunes models, ensuring their accuracy and relevance for various use cases.

NEW QUESTION # 175
......
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