UiPath-AAAv1 Practice Questions

A.  

Configure escalation behavior entirely within the outcome behavior section, specifying how each resolution should be handled.

B.  

Directly assign an escalation recipient to ensure proper routing, which eliminates the need for agent-specific prompts in the escalation logic.

C.  

Utilize required fields in the inputs section of the escalation to define conditions for triggering escalations dynamically.

D.  

Add a prompt in the properties panel to help the agent determine the appropriate circumstances for using the escalation.

A.  

Process operations teams and system admins

B.  

Process excellence analysts optimizing performance

C.  

Automation developers in the Center of Excellence

D.  

Process owners in business teams

A.  

Brainstorming automation use cases without validating personas or critically evaluating existing processes, focusing purely on agent capabilities.

B.  

Understanding the daily pain points and inefficiencies of the selected role to identify tasks that consume unnecessary time and potential gains from agent intervention.

C.  

Starting immediately with agent behavior prototyping using tools like the Agents designer canvas in Studio Web without assessing mapped automations or impacted systems.

D.  

Generalizing automation opportunities across all processes and roles without tailoring solutions based on specific personas or organizational contexts.

A.  

Maestro embeds external agents as inline code scripts inside the BPMN file and relies on each provider's runtime instead of Maestro's orchestration engine.

B.  

Maestro is a workflow engine similar to UiPath Studio, but it only allows you to invoke Agentic and Integration tasks.

C.  

Maestro deploys agents from UiPath and external providers—such as LangChain, CrewAI, or Agentforce—through one consistent framework that includes human-in-the-loop orchestration.

D.  

Maestro deploys only UiPath-built agents in robot-driven processes; any third-party agents must be integrated through external platforms without human checkpoints.

A.  

Use robots (RPA) for the structured, repetitive tasks, leveraging their rule-based approach for reliability and precision, while agents handle the unstructured processes by using their adaptive decision-making capabilities.

B.  

Use agents exclusively, as they can cover both structured workflows and dynamic environments due to their probabilistic and adaptive nature.

C.  

Use robots (RPA) exclusively, as they are capable of adapting to dynamic workflows with exception handling and learning capabilities.

D.  

Use agents for the structured, repetitive tasks, as they can follow deterministic rules efficiently while robots (RPA) handle unstructured workflows requiring adaptability, decision-making capabilities and contextual awareness.

A.  

A poorly defined agent story enables developers to identify improvement opportunities

B.  

A detailed agent story is only necessary when showcasing the agent's functionality to key stakeholders, rather than guiding the development process

C.  

An unclear agent story helps SMEs and stakeholders understand the potential risks associated with the agent

D.  

A good agent story helps the developers who will build the agent to focus on the essential features that deliver value

A.  

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.

B.  

The LLM's interpretation is solely based on the frequency of terms within the training dataset, rendering technical nuances irrelevant during generation.

C.  

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.

D.  

The LLM does not rely on tokenization for understanding prompts; instead, misinterpretation arises from inadequate pre-programmed definitions of technical terms.

A.  

Agents fail to execute tasks when information or processes change.

B.  

Agents adapt to changing conditions by learning.

C.  

Agents rely solely on static rules without contextual learning.

D.  

Agents require complete human assistance whenever processes change.

A.  

Add generic examples to allow the agent to experiment with the step format.

B.  

Enable the agent to generate longer troubleshooting guides for completeness.

C.  

Provide clear instructions to make steps actionable, concise, and free of redundancies.

D.  

Avoid explaining each step in detail to simplify the prompt.

A.  

Configuration is limited to enabling Context Grounding without any integration with Orchestrator or folder permissions.

B.  

You can configure Context Grounding by creating indexes in Orchestrator, managing folder-level permissions, selecting an LLM from the LLM Gateway, and syncing data using the Update Context Grounding Index activity.

C.  

Context Grounding requires default settings without any options for index creation or LLM selection.

D.  

Context Grounding setup relies entirely on manual indexing and lacks automated sync capabilities.