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UiPath UiPath-AAAv1 Exam Syllabus Topics:

UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:

1. What is the significance of the "as-is" process map in identifying agentic automation opportunities?

A) It directly outlines the roles that agents will assume in the optimized process, ensuring alignment with automation requirements.
B) It serves as a finalized map of processes ready for automation, removing the need for further adjustments or workshops.
C) It defines the current way tasks are performed, helping to highlight inefficiencies, bottlenecks, and areas for improvement that can uncover automation potential.
D) It establishes the goals of the new process, serving as a foundation to later create the "to-be" process map.

2. While configuring an Integration Service activity as a tool for your agent in Studio Web, how should you set up the activity so the agent can decide the value of a required field (e.g. Channel Id) at runtime based solely on instructions in the prompt?

A) Leave the field's input method on Prompt (the default) and keep or refine the tool description; this lets the agent infer the value during execution.
B) Change every field, including Channel Id, to Argument because an agent cannot infer any field values without explicit arguments.
C) Change every field, including Channel Id, to Variable because an agent cannot infer any field values without explicit arguments.
D) Declare the field as an output argument in Data Manager so the agent can feed a value back into the tool.

3. In which scenario is a deterministic evaluation more appropriate than a model-graded one?

A) When the correct output is known and fixed.
B) When the response quality depends on user satisfaction.
C) When open-ended reasoning needs to be scored.
D) When evaluating the tone and helpfulness of agent responses.

4. A developer is working on fine-tuning an LLM for generating step-by-step automation guides. After providing a detailed example prompt, they notice inconsistencies in the way the LLM interprets certain technical terms. What could be the reason for this behavior?

A) The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.
B) The inconsistency is related to the token limit defined for the prompt's length, which affects the LLM's ability to complete a response rather than its understanding of technical terms.
C) The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.
D) The LLM's tokenization process may have split complex technical terms into multiple tokens, causing slight variations in how the model interprets and weights their relationships within the context of the prompt.

5. Why would you choose the Argument input method for an activity field?

A) Applies one constant value you enter during design every time the agent executes the activity.
B) Prompts a person to supply the value each time the field is evaluated at runtime.
C) Receives a runtime value from an agent input argument defined earlier in the workflow.
D) Lets the agent infer the field value at runtime using the Description and its reasoning.

Solutions: