UiPath UiAAAv1 Exam Practice Questions (P. 1)
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Question #1
A developer is implementing a few-shot structured prompt for an email classification task. The prompt includes examples of email subjects labeled with their respective classifications, such as “Spam” or “Work”. What is the most important aspect to consider when selecting examples for the prompt?
- AUse random and unrelated examples to test the prompt’s robustness.
- BAlways use more than 10 examples, regardless of task complexity.
- CChoose examples that are diverse, relevant, and typical of the task’s expected input.
- DInclude examples with intentionally incorrect labels to improve training.
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Question #2
An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must: a. Retrieve relevant troubleshooting steps from the web. b. Notify the user via Slack if a solution is found. c. Escalate unresolved tickets via a custom process.
Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?
Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?
- AUse random input sampling across all tools and rely on the default “LLM-as-a-Judge” assertion.
- BCreate 30 evaluations for Slack notifications, 30 for web searches, and 30 for escalation processes.
- CGroup evaluations into sets: Valid web results triggering Slack notifications, Invalid web results triggering escalations, Edge cases.
- DCreate more than 30 evaluations for Slack notifications, more than 30 for web searches, and more than 30 for escalation processes.
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Question #3
What are the primary benefits of Context Grounding when querying data across multiple documents?
- AContext Grounding is limited to querying within a single document at a time.
- BContext Grounding only extracts random sentences without contextual understanding.
- CContext Grounding requires manual intervention for identifying connections between data points across documents.
- DContext Grounding understands relationships between data points across documents, enabling tasks like summarization, data comparison, and retrieval of highly relevant information.
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Question #4
An agent is built to extract customer feedback sentiment. You want to show the LLM how to classify it as ‘Positive’, ‘Neutral’, or ‘Negative’. Which few-shot design is most helpful?
- AInput: “The app is okay I guess.” → Output: “Text”
- BInput: “I love the new design, very intuitive!”
Output: “Positive”
Input: “Nothing special, just works.”
Output: “Neutral”
Input: “Terrible experience, won’t use again.”
Output: “Negative” - CList words like: “great, okay, bad” and map them to tone.
- DUse a multiple-choice table with numerical ratings from 1-5.
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Question #5
Which configuration area defines what the agent should do after a human resolves the escalation?
- AInputs description fields
- BAssignment recipient list
- CAgent Memory toggle
- DOutcome behavior section
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