UiPath UiPath-SAIv1

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions:

1. Which of the following OCR (Optical Character Recognition) engines is not free of charge?

A) Tesseract.
B) Microsoft OCR.
C) OmniPaqe.
D) Microsoft Azure OCR.

2. What is the definition of Machine Learning?

A) The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.
B) An area of machine learning concerned with artificial neural networks. These are a series of algorithms that aim to recognize relationships in a set of data through a process that mimics biological neural networks.
C) A branch of artificial intelligence that deals with analyzing, understanding, and generating human natural languages. For example, NLP enables computers to hear speech, read text, interpret the text
/speech or measure the sentiment.
D) A sub-field of artificial intelligence that enables systems to learn from data. Systems learn from previous experience and information to deduce and predict future information. To do this they use algorithms that learn to perform a specific task without being explicitly programmed.

3. Which statement accurately describes out-of-the-box models in UiPath?

A) Out-of-the-box models are only available for specific languages and cannot be adapted for multilingual documents.
B) Out-of-the-box models are custom-built models created specifically for each project.
C) Out-of-the-box models require extensive training and customization before they can be used effectively.
D) Out-of-the-box models are pre-trained models that cover a wide range of document types.

4. What are the two main data extraction methodologies used in document understanding processes?

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

5. Which is a high-level view of the tabs within an AI Center project?

A) Dashboard. Datasets, Data Labeling. ML Packages. Pipelines, ML Skills, and ML Logs.
B) Datasets, Data Labeling. ML Packages, ML Training, ML Evaluation, ML Skills, and ML Logs.
C) Datasets. Data Labeling. ML Packages. Pipelines, and ML Skills.
D) Dashboard. Datasets. ML Packages. ML Training. ML Evaluation, and ML Logs.

Solutions: