FREE PDF UIPATH - UIPATH-SAIAV1 USEFUL EXAMCOLLECTION DUMPS

Free PDF UiPath - UiPath-SAIAv1 Useful Examcollection Dumps

Free PDF UiPath - UiPath-SAIAv1 Useful Examcollection Dumps

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UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q163-Q168):

NEW QUESTION # 163
Which of the following statements is correct in the context of migrating a schema from Document Manager to a Modern Project?

  • A. If you import a schema into a document type that already contains a schema, the import will be successful and the schema will be updated with the missing field.
  • B. If you import a schema into a document type that already contains a schema, the import will fail.
  • C. If you import a schema into a document type that already contains a schema, the import will fail only if documents were already uploaded.
  • D. If you import a schema into a document type that already contains a schema, the import will be successful and the schema will be overridden.

Answer: D

Explanation:
When migrating a schema from Document Manager to a Modern Project, UiPath will override any existing schema associated with the document type if you attempt to import a new schema. This behavior is confirmed in UiPath's handling of document types and schema imports, where the new schema will replace the old one, ensuring that the latest version is applied to the project. This helps maintain consistency in schema configurations during migrations, especially when schemas are updated or need to be standardized


NEW QUESTION # 164
In UiPath Communications Mining, what does the Reports section contain?

  • A. Tools for message analytics and monitoring.
  • B. Tools for comparing different model versions.
  • C. Tools for evaluating label and entity performance.
  • D. Tools for evaluating model performance.

Answer: A

Explanation:
In UiPath Communications Mining, the Reports section provides a variety of tools for analyzing and monitoring messages within a dataset. This includes functionalities like label summaries, trends, and message segmentation, which allow users to gain insights into message volumes, sentiment trends, and other analytical metrics. The Reports section is integral for tracking the performance of messages and the overall dataset, making it useful for monitoring communication channels


NEW QUESTION # 165
In the general fields training, what actions does the Communications Mining Train feature guide you through?

  • A. It guides you to focus solely on model refinement.
  • B. It guides you to start entity training without a dataset.
  • C. It guides you through the completion of model training before starling entity training.
  • D. It guides you right from the moment you create a dataset with the next best action to take to advance your general fields training.

Answer: D

Explanation:
his is confirmed in the UiPath documentation, where the "Train" feature guides users from the dataset creation stage through the necessary steps to optimize the training process. The system recommends next best actions based on progress to ensure efficient and focused training


NEW QUESTION # 166
Why might labels have bias warnings in UiPath Communications Mining, even with 100% precision?

  • A. They were trained using the "Shuffle" option extensively.
  • B. They were trained using the "Search" option extensively.
  • C. They lack training examples.
  • D. They have low recall.

Answer: C

Explanation:
Labels in UiPath Communications Mining are user-defined categories that can be applied to communications data, such as emails, chats, and calls, to identify the topics, intents, and sentiments within them1. Labels are trained using supervised learning, which means that users need to provide examples of data that belong to each label, and the system will learn from these examples to make predictions for new data2. However, not all labels are equally easy to train, and some may requiremore examples than others to achieve good performance. Labels that have bias warnings are those that have relatively low average precision, not enough training examples, or were labelled in a biased manner3. Precision is a measure of how accurate the predictions are for a given label, and it is calculated as the ratio of true positives (correct predictions) to the total number of predictions made for that label. A label with 100% precision means that all the predictions made for that label are correct, but it does not necessarily mean that the label is well-trained. It could be that the label has very few predictions, or that the predictions are only made on a subset of data that is similar to the training examples. This could lead to overfitting, which means that the label is too specific to the training data and does not generalize well to new or different data. Therefore, labels with 100% precision may still have bias warnings if they lack training examples, because this indicates that the label is not representative of the underlying data distribution, and may miss important variations or nuances that could affect the predictions. To improve the performance and reduce the bias of these labels, users need to provide more and diverse examples that cover the range of possible scenarios and expressions that the label should capture.
References: 1: Communications Mining Overview 2: [Creating and Training Labels] 3: Understanding and Improving Model Performance : [Precision and Recall] : [Overfitting and Underfitting] : Fixing Labelling Bias With Communications Mining


NEW QUESTION # 167
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. Datasets, Data Labeling. ML Packages, ML Training, ML Evaluation, ML Skills, and ML Logs.
  • C. Dashboard. Datasets, Data Labeling. ML Packages. Pipelines, ML Skills, and ML Logs.
  • D. Datasets. Data Labeling. ML Packages. Pipelines, and ML Skills.

Answer: C

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 # 168
......

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