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UiPath-SAIv1 Exam Forum - UiPath-SAIv1 Printable PDF

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UiPath Certified Professional Specialized AI Professional v1.0 Sample Questions (Q14-Q19):

NEW QUESTION # 14
When dealing with variable-length data, or data spanning over multiple pages of the document (e.g. item tables), what is the recommended data extraction methodology to be used?

A. Hybrid data extraction.
B. Model-based data extraction.
C. Rule-based data extraction.
D. Manual data extraction.

Answer: B

NEW QUESTION # 15
What is the purpose of the "Explore" phase in UiPath Communications Mining?

A. To provide each label/entity in a taxonomy with enough training examples so the model can make accurate predictions at scale.
B. To review the clusters of similar communications from a data set that unsupervised learning automatically found.
C. To fully review and correctly tag the model version, regardless if it's "Live" or "Staging".
D. To use the bulk label functionality, a helpful tool to quickly train the model when searching for specific terms.

Answer: A

NEW QUESTION # 16
Which role consumes ML Skills within customized workflows in Studio using the ML Skill activity from the UiPath.MLServices.Activities package?

A. RPA Developer.
D Process Controller
B. Administrator.
C. Data Scientist.

Answer: A

Explanation:
According to the UiPath documentation portal1, the RPA Developer is the role that consumes ML Skills within customized workflows in Studio using the ML Skill activity from the UiPath.MLServices.Activities package. The RPA Developer is responsible for designing, developing, testing, and deploying automation workflows using UiPath Studio and other UiPath products. The RPA Developer can use the ML Skill activity to retrieve and call all ML Skills available on the AI Center service and request them within the automation workflows. The ML Skill activity allows the RPA Developer to pass data to the input of the skill, test the skill, and receive the output of the skill as JSON response, status code, and headers2. Therefore, option C is the correct answer, as it describes the role and the activity that are related to consuming ML Skills in Studio. Option A is incorrect, as the Data Scientist is the role that creates and trains ML models using AI Center or other tools, and publishes them as ML Packages or OS Packages1. Option B is incorrect, as the Administrator is the role that manages the AI Center service, such as configuring the infrastructure, setting up the permissions, and monitoring the usage and performance1. Option D is incorrect, as the Process Controller is the role that deploys ML Packages or OS Packages as ML Skills, and manages the versions, the endpoints, and the API keys of the skills1.
References: 1 AI Center - User Personas 2 Activities - ML Skill

NEW QUESTION # 17
Which UiPath Communications Mining model performance factor assesses the proportion of the entire dataset that has informative label predictions?

A. Average label performance.
B. Coverage.
C. Balance.
D. Underperforming labels.

Answer: B

Explanation:
According to the UiPath Communications Mining documentation, coverage is one of the four main factors that contribute to the model rating, which is a holistic measure of the model's performance and health.
Coverage assesses the proportion of the entire dataset that has informative label predictions, meaning that the predicted labels are not generic or irrelevant. Coverage is calculated as the percentage of verbatims (communication units) that have at least one informative label out of the total number of verbatims in the dataset. A high coverage indicates that the model is able to capture the main topics and intents of the communications, while a low coverage suggests that the model is missing important information or producing noisy predictions.
References:
* Communications Mining - Understanding and improving model performance
* Communications Mining - Model Rating
* Communications Mining - It's All in the Numbers - Assessing Model Performance with Metrics

NEW QUESTION # 18
What differentiates UiPath Communications Mining general fields trained from scratch from general fields that are pre-trained?

A. The trained-from-scratch general fields have better accuracy, while the pre-trained general fields have a longer training time.
B. The trained-from-scratch general fields are entirely user-trained, while the ones that are pre-trained are typically based on a set of standard or custom-defined rule
C. The trained-from-scratch general fields depend on the model version, while the ones that are pre-trained do not.
D. The trained-from-scratch general fields need to be normalized to be used in downstream automation, while the ones that are pre-trained can only be used in dashboards within the platform.

Answer: B

Explanation:
In UiPath Communications Mining, general fields that are trained from scratch require user-defined inputs and training data, making them highly customizable but dependent on the specific data provided by the user.
In contrast, pre-trained general fields are based on predefined rules and training models developed by UiPath.
These pre-trained fields offer out-of-the-box functionality and are optimized for common use cases, whereas user-trained fields offer more flexibility to meet specific business requirements.
(Source: UiPath Communications Mining documentation)

NEW QUESTION # 19
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