Question # 1
| What is the Machine Learning Extractor? | | A. A specialized model that can recognize multiple languages in the same document using
API calls to a Hugging Face model with over 250 languages. | | B. An extraction model that can be enabled and trained in Al Center. For better accuracy.
25 documents per model are recommended to train the model. | | C. A tool using machine learning models to identify and report on data targeted for data
extraction. | | D. A tool that helps extract data from different document structures, and is particularly
useful when the same document has multiple formats. |
D. A tool that helps extract data from different document structures, and is particularly
useful when the same document has multiple formats.
Question # 2
| What is the difference between the Document Understanding Process and the Document
Understanding Framework? | | A. The Document Understanding Framework contains the activities that can be used in a
Library, while the Document Understanding Process is the template that can be found in Studio. | | B. The Document Understanding Framework contains the activities that can be used in a
Process, while the Document Understanding Process is the template that can be found in
Studio. | | C. The Document Understanding Process contains the activities that can be used in a
Library, while the Document Understanding Framework is the template that can be found in
Studio. | | D. The Document Understanding Process contains the activities that can be used in a
Process, while the Document Understanding Framework is the template that can be found
in Studio. |
D. The Document Understanding Process contains the activities that can be used in a
Process, while the Document Understanding Framework is the template that can be found
in Studio.
Question # 3
| What happens during the Classify stage of the Document Understanding Framework? | | A. The OCR engine is used to extract text from the image document. | | B. The extracted data is exported as a dataset. | | C. The target fields are extracted from the document and sent to Action Center for human
validation. | | D. The documents are included in one of the taxonomy document types or skipped. |
D. The documents are included in one of the taxonomy document types or skipped.
Explanation: According to the UiPath documentation, the Classify stage of the Document
Understanding Framework is used to automatically determine what document types are
found within a digitized file. The document types are defined in the project taxonomy, which
is a collection of all the labels and fields applied to the documents in a dataset. The
Classify stage uses one or more classifiers, which are algorithms that assign document
types to files based on their content and structure. The classifiers can be configured and
executed using the Classify Document Scope activity, which also allows for document type
filtering, taxonomy mapping, and minimum confidence threshold settings. The Classify stage outputs the classification information in a unified manner, irrespective of the source
of classification. The documents that are classified are then sent to the next stage of the
framework, which is Data Extraction. The documents that are not classified or skipped are
either excluded from further processing or sent to Action Center for human validation and
correction.
Question # 4
| What is supervised learning? | A. Supervised learning is a machine learning paradigm in which algorithms try to solve a
problem only by trial and error and using a system of rewards and punishments.
There is no need for labeled input/output pairs to be presented. Instead, the focus is on
finding a balance between exploration (of uncharted territory) and exploitation (of current
knowledge). | B. Supervised learning is a machine learning paradigm in which algorithms try to solve a
problem in an uncertain, potentially complex environment only by trial and error and using a
system of
rewards and punishments.
There are no correct answers, but feedback is given in the form of rewards and penalties. | C. Supervised learning is a machine learning paradigm with the goal of learning a function
that maps input variables with output variables.
In every case there is a correct answer, so the aim is to train the model until it reaches an
acceptable level of performance in predicting the outcome, at which point the learning stops. | D. Supervised learning is a machine learning paradigm that refers to algorithms that learn
patterns from unlabeled data.
There are only input variables, but no corresponding output variables. The goal of the
algorithm is to model the underlying structure of the data, but there are no correct answers
and no
teachers. |
C. Supervised learning is a machine learning paradigm with the goal of learning a function
that maps input variables with output variables.
In every case there is a correct answer, so the aim is to train the model until it reaches an
acceptable level of performance in predicting the outcome, at which point the learning stops.
Explanation: Supervised learning is one of the most popular and widely used machine
learning approaches. It involves providing the algorithm with labeled input/output pairs,
which serve as examples of the desired behavior or outcome. The algorithm then learns a
function that can generalize from these examples and make predictions for new, unseen
data. Supervised learning can be used for tasks such as classification, regression, and
anomaly detection. Some common supervised learning algorithms are linear regression,
logistic regression, decision trees, support vector machines, and neural networks.
Question # 5
| How long does the typical Machine Learning model deployment process take in UiPath AI
Center? | | A. Less than 5 minutes. | | B. Between 5 and 10 minutes. | | C. Between 5 and 10 minutes. | | D. More than 15 minutes. |
C. Between 5 and 10 minutes.
Explanation: The typical machine learning model deployment process in UiPath AI Center
usually takes between 10-15 minutes1. This process involves wrapping the model in
UiPath’s serving framework and deploying it within a namespace on AI Fabric’s Kubernetes
cluster that is only accessible by your tenant1. Please note that the actual time may vary depending on the complexity of the model and other factors.
Question # 6
| Under what condition can a dataset be edited in UiPath AI Center? | | A. If it is not being used in any active pipeline. | | B. If it has not been modified within the last 24 hours. | | C. There are no restrictions in editing a dataset. | | D. If it is not linked to any data labeling session. |
A. If it is not being used in any active pipeline.
Explanation: According to the UiPath documentation, a dataset is a folder of storage
containing arbitrary sub-folders and files that allows machine learning models in your
project to access new data points. You can edit a dataset’s name, description, or content
from the Datasets > [Dataset Name] page, by clicking Edit dataset. However, you can only
edit a dataset if it is not currently being used in an active pipeline. A pipeline is a sequence
of steps that defines how to train, test, and deploy a machine learning model. If a dataset is
being used in an active pipeline, you will see a lock icon next to it, indicating that it cannot
be edited. You can either wait for the pipeline to finish or stop it before editing the dataset.
Question # 7
| When is it recommended to use Main-ActionCenter in the context of the Document
Understanding Process? | | A. When implementing an attended process. | | B. When testing locally or implementing an attended process. | | C. When testing locally. | | D. When testing locally or implementing an unattended process. |
B. When testing locally or implementing an attended process.
Explanation: Main-ActionCenter is a workflow that allows you to create and manage
Document Understanding actions in Action Center, which is a web application that enables
human intervention in automation processes. You can use Main-ActionCenter when you
want to test your Document Understanding process locally, or when you want to implement
an attended process that requires human validation or classification of documents. Main-
ActionCenter is not recommended for unattended processes, as they do not involve human
interaction.
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