What does the Data Extraction Scope activity do?
A. Empowers the closing of the feedback loop to any data extraction algorithm capable of learning.
B. Provides a scope for extractor activities, enabling their configuration according to the document types defined in the taxonomy.
C. Retrieves the text from any PDF or image, using, only if necessary, the OCR engine.
D. Presents a document processing specific user interface for data validation and correction.
What are the correct steps for creating an ML Skill?
A. Create a project in Document Understanding, create a dataset, create a data labeling session, create an ML package, create a pipeline, deploy an ML Skill, and check ML logs.
B. Create a project in AICenter, create a folder, create a dataset, create a data labeling session, create an ML package, create a pipeline, and deploy an ML Skill.
C. Create a project in AICenter, create a dataset, create a data labeling session, create an ML package, create a pipeline, and deploy an ML Skill.
D. Create a project in AICenter, create a dataset, create a data labeling session, create an ML package, create a pipeline, deploy an ML Skill, and check ML logs.
What are the requirements that must be fulfilled by a custom ML package containing the training and evaluation component?
A. A folder containing the main.py file which implements the __init__(self) and predict(self, input) functions, the train.py file which implements the __init__(self), train(self, training_directory), evaluate(self, evaluation_directory) functions.
B. A folder containing the main.py file which implements the __init__(self) and predict(self, input) functions, the train.py file which implements the __init__(self), train(self, training_directory), evaluate(self, evaluation_directory), save(self), and process_data(self, input_directory) functions, and the requirements.txt file which lists the dependencies needed to run the model.
C. A folder containing the main.py file which implements the __init__(self), evaluate(self, evaluation_directory), and predict(self, input) functions, and the requirements.txt file which lists the dependencies needed to run the model.
D. A folder containing the main.py file which implements the __init__(self), train(self, training_directory), and predict(self, input) functions, and the requirements.txt file which lists the dependencies needed to run the model.
In a process where employees need to read emails from customers, then process invoices attached as part of day-to-day work, what combination of UiPath solutions is most suited to structure data from these two types of inputs?
A. UiPath Communications Mining and UiPath Document Understanding.
B. UiPath Document Understanding and UiPath Process Mining.
C. UiPath Document Understanding and UiPath Task Mining.
D. UiPath Communications Mining and UiPath Task Mining.
Which activity from the UiPath.IntelligentOCR.Activities Package allows you to retrieve text from PDF or image files?
A. Data Extraction Scope activity.
B. Present Classification Station activity.
C. Classify Document activity.
D. Digitize Document activity.
What is Computer Vision?
A. The theory and development of computer systems that are able to perform tasks that normally require human intelligence and decision making.
B. 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.
C. 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.
D. A field of artificial intelligence that enables computers to gain high-level understanding from digital images or videos. If AI is the brain, then this is the eye that enables the computer to observe and understand. It works the same as the human eye.
What are the requirements that must be fulfilled by a package containing only the serving component?
A. A folder containing the main.py file which implements the__init_ (self), train(self, training_directory), and predict(self, input) functions, and the requirements.txt file which lists the dependencies needed to run the model.
B. A folder containing the main.py file which implements the__init_ (self) and predict(self, input) functions.
C. A folder containing the main.py file which implements the__init_ (self), evaluate(self, evaluation_directory), and predict(self, input) functions, and the requirements.txt file which lists the dependencies needed to run the model.
D. A folder containing the main.py file which implements the__init_ (self) and predict(self, input) functions, and the requirements.txt file which lists the dependencies needed to run the model.
Which of the following is true about Label Suggestions in UiPath Communications Mining?
A. Label suggestions are identifiable through their double outline and dark fill.
B. If a label suggestion is wrong, it must be explicitly dismissed on the verbatim it was suggested on.
C. Label suggestions and label predictions serve the same purpose.
D. They will not be counted in Reports for analytics, or made available downstream for automation.
Which of the following statements is correct regarding ML (Machine Learning) model best practices?
A. Building an ML model doesn't require feature engineering. The best practice is simply inputting the raw data into the model for obtaining accurate predictions.
B. The best practice when building an ML model is to use as much data as possible, regardless of its quality or diversity. More data always leads to better model performance, even if the data is unbalanced or contains errors because the model has much more information it can learn from.
C. When building an ML model, it is best to train it on the entire dataset without splitting it into training, validation, and test sets. This approach allows the model to learn from all available data, resulting in optimal performance.
D. Collecting high-quality and diverse data is a best practice when building an ML model. This includes ensuring data cleanliness, addressing class imbalance if present, and validating data integrity. Splitting the entire dataset into training, validation, and test is also recommended.
What is a reason for pinning a UiPath Communications Mining Model?
A. To delete all other model versions.
B. To allow rollback of annotations to that model version.
C. To force the UI to show predictions from that model version in explore
D. To allow AB comparing of the statistics of that model version with another one.
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