Testking UiPath UiPath-ABAv1 Learning Materials, UiPath-ABAv1 Test Guide Online
Testking UiPath UiPath-ABAv1 Learning Materials, UiPath-ABAv1 Test Guide Online
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UiPath Certified Professional Automation Business Analyst Professional v1.0 Sample Questions (Q146-Q151):
NEW QUESTION # 146
During the User Acceptance Testing phase of Automation Implementation, if there are deviations from the expected outcomes, who is primarily responsible for logging those and working towards resolving those issues?
- A. The Operations Team, who would receive details on the Runbook, is responsible for logging any deviations and working with the Developer to fix these issues.
- B. The Solution Architect is responsible for logging all deviations and working with the Business Analyst to rectify them.
- C. The Business Analyst would log these deviations and work with the Automation Implementation Team to resolve the issues.
- D. The Developer, who runs the bot in the pre-production environment, is responsible for logging the deviations and working with the Solution Architect to solve them.
Answer: C
Explanation:
Role of the Business Analyst in UAT:
During the User Acceptance Testing (UAT) phase, the Business Analyst plays a key role in ensuring that the automation aligns with business requirements.
They are responsible for:
Logging deviations from expected outcomes.
Coordinating with the Automation Implementation Team to resolve issues.
Option Analysis:
Option A: Developers primarily address technical fixes but do not oversee logging during UAT.
Option B: Solution Architects focus on system design and may assist in technical adjustments but are not responsible for logging deviations.
Option D: The Operations Team handles the post-deployment phase and runbook management, not UAT testing.
Correct Option (C): The Business Analyst ensures deviations are identified, logged, and resolved in collaboration with the implementation team.
NEW QUESTION # 147
Where can you find details about business rules exceptions and how to be handled?
- A. In the Detailed As-Is Process Steps
- B. In the PDD, in the To-Be Process Description
- C. In the Traceability Matrix
- D. In the PDD, in the As-Is Process Description
Answer: B
Explanation:
Details about business rules exceptions and how they are to be handled can typically be found in the Process Design Document (PDD), specifically in the To-Be Process Description section. This section of the PDD outlines the future state of the process after automation, including how exceptions and business rules are managed. It provides a clear guide on how the automated process should handle different scenarios, including exceptions that may arise due to business rules1.
References: The explanation is based on the UiPath Documentation Portal's guidelines on PDD creation and the handling of exceptions within the automation process1.
NEW QUESTION # 148
Which of the following is the correct order of steps in the process of creating a new process app in UiPath Process Mining?
- A. Create new app > Selecting the app template > Defining the app properties > Selecting the data source > Review details > Create App
- B. Create new app Selecting the data source > Selecting the app template > Defining the app properties > Review details > Create App
- C. Create new app > Defining the app properties > Selecting the data source > Selecting the app template
>Review details > Create App - D. Create new app > Selecting the app template > Defining the app properties > Review details > Selecting the data source > Create App
Answer: A
Explanation:
The correct order of steps in the process of creating a new process app in UiPath Process Mining is as follows:
Create new app > Selecting the app template > Defining the app properties > Selecting the data source > Review details > Create App. This sequence ensures that the foundational elements of the app are established before specifying the data source, which helps in aligning the data with the defined properties and template.References: UiPath Documentation on Process Mining athttps://docs.uipath.com/.
NEW QUESTION # 149
What is same-screen detection for actions recorded in an UiPath Task Capture diagram?
- A. After finishing the capture process. Task Capture offers to detect actions per all documents that contain similar screenshots and also offers to merge them. The actions can also be manually reviewed prior to merging
- B. After finishing the capture process. Task Capture offers to detect actions per all documents that contain similar screenshots and also offers to merge them. The merge is automatic, thus making the process fast and efficient
- C. Prior to starting the capture process, Task Capture analyzes the applications used and groups them by similarity of appearance, so that during the recording the screenshots will be automatically grouped
- D. During the capture process Task Capture automatically detects actions that contain similar screenshots and also merges them
Answer: A
Explanation:
In an UiPath Task Capture diagram, same-screen detection for actions recorded is described by the feature where after finishing the capture process, Task Capture offers to detect actions per all documents that contain similar screenshots and also offers to merge them. The actions can also be manually reviewed prior to merging. This feature ensures that similar actions are grouped, enhancing the clarity and efficiency of the process documentation.
Reference: UiPath Documentation on Task Capture at https://docs.uipath.com/.
NEW QUESTION # 150
What is the difference between training an ML model using a CPU versus using a GPU in UiPath Cloud Platform?
- A. Training a model using a GPU is around five times faster than using a CPU
- B. You can train multiple models on the same GPU at the same time
- C. Training a model using a GPU is around five times slower than using a CPU
- D. Training a model using a GPU provides more accurate results
Answer: A
Explanation:
In the context of UiPath Cloud Platform, training a machine learning (ML) model using a GPU (Graphics Processing Unit) is significantly faster than using a CPU (Central Processing Unit). This is because GPUs are designed to handle parallel processing, which is highly beneficial for the matrix and vector computations required in ML model training. GPUs can perform more calculations simultaneously compared to CPUs, leading to faster training times, especially with large and complex datasets1.
The UiPath documentation specifically recommends using GPU training for large and production datasets, as CPU training is much slower and should be used sparingly, for small datasets for demo or testing purposes1.
This aligns with the general understanding that for deep learning training, GPUs should be used due to their significant speed advantage over CPUs23.
References: The information is verified as per the UiPath Documentation Portal and other reliable sources that discuss the advantages of using GPUs over CPUs for ML model training123.
NEW QUESTION # 151
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