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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q141-Q146):

NEW QUESTION # 141
What is the primary advantage of the One Click Extraction feature in UiPath's Document Understanding interface?

A. It allows training document extractors without manually creating Datasets and Pipelines, but you need to manually deploy the ML Skills in AI Center.
B. It automates the creation of document extractors by automatically creating Datasets, Pipelines, and ML Skills but requires manual creation of Pipelines.
C. It allows training document extractors without manually creating Datasets, Pipelines, and ML Skills in AI Center.
D. It provides a simplified way to manually create Datasets, Pipelines, and Datasets for document extraction.

Answer: C

Explanation:
Reference: UiPath One Click Extraction

NEW QUESTION # 142
What is a recommended approach for increasing response accuracy when asking the Generative Extractor a question?

A. Combine arithmetic operations with the question to create a better context.
B. Combine complex if-then-else type logic questions to create a better context.
C. Specify an output format to standardize the response.
D. Request confidence levels for predictions.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When using theGenerative Extractorin Document Understanding, the best way to increase accuracy and reliability is byspecifying a clear output format. This helps the model understand what kind of response is expected and improves parsing consistency.
* Example: "What is the invoice number? Please return in the format: InvoiceNumber: <value>"
* UiPath Documentation Reference:Using Generative Extractor - Document Understanding

NEW QUESTION # 143
Under what condition can a dataset be edited in UiPath AI Center?

A. If it is not being used in any active pipeline.
B. There are no restrictions in editing a dataset.
C. If it is not linked to any data labeling session.
D. If it has not been modified within the last 24 hours.

Answer: A

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.
References:
AI Center - Managing Datasets
AI Center - About Datasets
AI Center - About Pipelines

NEW QUESTION # 144
Which is a high-level view of the tabs within an AI Center project?

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

Answer: B

Explanation:
A high-level view of the tabs within an AI Center project is as follows:
Dashboard: This tab provides an overview of the project's status, such as the number of datasets, pipelines, packages, skills, and logs, as well as the AI Units consumption and quota.
Datasets: This tab enables you to upload, view, and manage the datasets that are used for training and evaluating the ML models within the project. A dataset is a folder of storage containing arbitrary files and sub- folders1.
Data Labeling: This tab enables you to upload raw data, annotate text data in the labeling tool (for classification or entity recognition), and use the labeled data to train ML models. It is also used by the human reviewer to re-label incorrect predictions as part of the feedback process2.
ML Packages: This tab enables you to upload, view, and manage the ML packages and package versions within the project. An ML package is a group of package versions of the same package type, and a package version is a trained model that can be deployed to a skill3.
Pipelines: This tab enables you to create, view, and manage the pipelines and pipeline runs within the project. A pipeline is a description of an ML workflow, including the functions and their order of execution, and a pipeline run is an execution of a pipeline based on code provided by the user4.
ML Skills: This tab enables you to deploy, view, and manage the ML skills within the project. An ML skill is a live deployment of a package version, which can be consumed by an RPA workflow using an ML skill activity in UiPath Studio5.
ML Logs: This tab enables you to view and filter the logs related to the project, such as the events, messages, and errors that occurred during the pipeline runs, skill deployments, and skill executions6.
References:
1: About Datasets 2: About Data Labeling 3: About ML Packages 4: About Pipelines 5: About ML Skills 6: About ML Logs

NEW QUESTION # 145
What are the out-of-the-box model types available in AI Center?

A. Custom training, fine-tunable, and reviewed.
B. Pre-trained, custom training, and fine-tunable.
C. Pre-trained, fine-tunable, and reviewed.
D. Pre-trained, custom training, and reviewed.

Answer: B

Explanation:
According to the UiPath documentation, AI Center provides three types of out-of-the-box model types that can be used for different purposes and scenarios1:
Pre-trained: These are models that are already trained on a large and diverse dataset and can be used as-is for inference or prediction. They do not require any additional data or training from the user. Examples of pre- trained models are Language Detection, Sentiment Analysis, and Question Answering.
Custom training: These are models that allow the user to train them on their own data using the AI Center UI or API. They require the user to upload a dataset, configure the training parameters, and monitor the training progress and results. Examples of custom training models are Image Classification, Text Classification, and Named Entity Recognition.
Fine-tunable: These are models that are pre-trained on a general dataset but can be further trained or fine- tuned on a specific dataset provided by the user. They offer the best of both worlds: the benefit of using a pre- trained model and the flexibility of customizing it to the user's needs. Examples of fine-tunable models are Object Detection, Text Summarization, and Language Translation.
References:
1: Out-of-the-Box Packages

NEW QUESTION # 146
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