UIPATH-SAIAV1 TEST OBJECTIVES PDF | UIPATH-SAIAV1 ONLINE TRAINING

UiPath-SAIAv1 Test Objectives Pdf | UiPath-SAIAv1 Online Training

UiPath-SAIAv1 Test Objectives Pdf | UiPath-SAIAv1 Online Training

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As we mentioned above that the UiPath Specialized AI Associate Exam (2023.10) (UiPath-SAIAv1) exam questions is provided to students in three different formats. The first format is UiPath Specialized AI Associate Exam (2023.10) PDF dumps which is printable and portable. It means students can save it on their smart devices like smartphones, tablets, and laptops. The UiPath Specialized AI Associate Exam (2023.10) (UiPath-SAIAv1) PDF dumps format can be printed so that candidates don't face any issues while preparing for the UiPath Specialized AI Associate Exam (2023.10) exam.

UiPath Specialized AI Associate Exam (2023.10) Sample Questions (Q106-Q111):

NEW QUESTION # 106
While training a UiPath Communications Mining model, the Search feature was used to pin a certain label on a few communications. After retraining, the new model version starts to predict the tagged label but infrequently and with low confidence.
According to best practices, what would be the correct next step to improve the model's predictions for the label, in the "Explore" phase of training?

  • A. Use the "Search" feature to pin the label to more communications.
  • B. Use the "Low confidence" training mode to pin the label to more communications.
  • C. Use the "Rebalance" training mode to pin the label to more communications.
  • D. Use the 'Teach" training mode to pin the label to more communications.

Answer: D

Explanation:
According to the UiPath documentation, the 'Teach' training mode is used to improve the model's predictions for a specific label by pinning it to more communications that match the label's criteria. This helps the model learn from more examples and increase its confidence and accuracy. The 'Teach' mode also allows you to unpin the label from communications that do not match it, which helps the model avoid false positives. The other training modes are not as effective for this purpose, as they either focus on different aspects of the model performance or do not provide enough feedback to the model.
References:
Model training and labelling best practice
Overview of the model training process
Model Training FAQs


NEW QUESTION # 107
When a parent label is deleted in UiPath Communications Mining, what happens to the training data tor that label?

  • A. The parent label and any child labels to that parent label are removed from any reviewed messages, any messages with updated annotations are flagged for review.
  • B. The parent label is removed from any reviewed messages but the child labels to that parent label remain.
  • C. The parent label is removed from any reviewed messages, messages with any child labels to that parent label are flagged for review.
  • D. The parent label and any child labels to that parent label are removed from any reviewed messages.

Answer: A

Explanation:
In UiPath Communications Mining, when a parent label is deleted, both the parent and its child labels are removed from the reviewed messages. Additionally, any messages with updated annotations that were associated with those labels are flagged for review to ensure consistency in the training data


NEW QUESTION # 108
Which scenario would be best accomplished using unattended automation?

  • A. Generating invoices that require manual approval before issuing.
  • B. Providing on-demand assistance to users for specific activities.
  • C. Running reports and sending them via email to stakeholders overnight.
  • D. Resolving customer queries with real-time input from support agents.

Answer: C

Explanation:
Unattended automation is ideal for tasks that can run independently without human intervention, such as scheduled activities during off-hours. Running reports and emailing stakeholders overnight is a perfect use case for unattended bots, which can execute the process autonomously.
Reference: UiPath Orchestrator Unattended Automation


NEW QUESTION # 109
Why is it important to understand the potential value UiPath Communications Mining can enable prior to training?

  • A. To forecast the number of trainers that are required to achieve an excellent performing model.
  • B. To ensure the objectives are focused on delivering targeted value and the model's taxonomy is aligned to value realization.
  • C. To calculate the potential reduction in data storage costs due to model training.
  • D. To estimate the amount of time required to build an excellent performing model.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Before training a model in Communications Mining, it iscrucial to align the taxonomy and labeling strategy with business goalsto ensure the outputs provide measurable value. This preparation stage helps define KPIs, label definitions, and ensures the AI output leads to actionable automation or insight.
* Focusing on value from the startprevents wasted effortand allows prioritization of the most impactful communication types.
* UiPath Documentation Reference:Communications Mining - Best Practices for Training


NEW QUESTION # 110
What is supervised learning?

  • A. 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 ofrewards and punishments.There are no correct answers, but feedback is given in the form of rewards and penalties.
  • B. 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).
  • 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 noteachers.

Answer: C

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.
References:
UiPath AI Fabric - Machine Learning Concepts
UiPath Document Understanding - Machine Learning Models
UiPath Communications Mining - Overview


NEW QUESTION # 111
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