[2026] Pass UiPath-AAAv1 Exam – Real Questions & Answers [Q16-Q35] – IT Certification Exam Questions & Answers

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[2026] Pass UiPath-AAAv1 Exam – Real Questions and Answers

UiPath-AAAv1 Exam Questions Get Updated [2026] with Correct Answers

NO.16 For what primary reason should you supply a description for every input and output argument in an agent?

Descriptions cause Orchestrator triggers to pre-populate the arguments automatically, eliminating manual mapping.

Clear descriptions help the agent understand how to use each argument effectively while generating or returning results.

Adding descriptions forces Studio Web to treat all arguments as mandatory fields that block deployment if left empty.

Argument descriptions are required only for input arguments; output arguments are inherently self- explanatory and do not benefit from them.

NO.17 What is the defining characteristic of few-shot prompting?

It relies on intermediate reasoning steps to guide the model’s response.

It requires the model to generate a response with no examples or instructions.

It links multiple prompts together in a sequential workflow.

It uses several examples to help the model understand the task better.

NO.18 Which of the following is an essential aspect of crafting a comprehensive agent story during the validation stage?

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

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

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

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

NO.19 What steps must be completed when creating evaluations from scratch for a new evaluation set in UiPath?

Assign evaluators immediately after creating the new evaluation set name, then configure inputs and expected outputs later.

Add a name to the evaluation set, provide input values and expected output, save each evaluation, and assign evaluators before running the evaluation set.

The evaluation set can only be created using imported JSON data from previous evaluations of other agents.

Once the evaluation set is created, all included evaluations are automatically scored based only on input values and expected outputs.

NO.20 What is a System Prompt?

A System Prompt is a technical script integrated into the automation process that determines tool usage and escalation protocols without considering natural language descriptions.

A System Prompt is a predefined list of actions and commands the agent strictly follows without adaptation or interaction over time.

A System Prompt allows a user to describe its role, goals, and constraints while specifying rules and guidelines for actions, including the use of tools, escalations, and context.

A System Prompt defines only the agent’s constraints but does not address goal-setting or sequencing steps.

NO.21 When adding an index for querying data stored in CSV files, what advanced feature does UiPath Context Grounding provide to optimize retrieval?

Support for structured queries tailored specifically for CSV data.

Automatic conversion of CSV data into native XLSX files for enhanced compatibility.

Streaming support for real-time ingestion and CSV query execution without indexing.

Embedding data from CSV files into JSON templates for improved semantic similarity.

NO.22 When creating an Action app, what is the purpose of defining the “Approve” and “Deny” outcomes within the Action schema?

To ensure the app validates search results and prevents faulty submissions.

To guide the agent’s next steps based on the review results of Input/Output properties.

To save user input as mandatory action schema properties during automation execution.

To dynamically update user-facing form labels with the action result.

NO.23 What is one of the key benefits of providing RAG as a service to UiPath generative AI experiences?

It reduces the risk of hallucination by referencing ground truth data stores.

It directly increases the LLM context window size without any interaction with knowledge bases.

It eliminates the need for knowledge bases by integrating all proprietary data directly into generative applications.

It exclusively provides access to historical data sources without supporting real-time updates.

NO.24 What is the primary recommendation for usinghttps://www.google.com/search?q=bpmn.uipath.comto access the Maestro modeling canvas?

Utilizehttps://www.google.com/search?q=bpmn.uipath.comexclusively for creating agentic processes with implementation capabilities.

Usehttps://www.google.com/search?q=bpmn.uipath.comas a sandbox since the tool does not offer implementation capabilities.

Usehttps://www.google.com/search?q=bpmn.uipath.comonly to import predefined process templates from Studio Web.

Always usehttps://www.google.com/search?q=bpmn.uipath.comas the primary tool for modeling processes with full implementation features.

NO.25 Why is mapping processes a critical step in identifying opportunities for agentic automation?

It prioritizes identifying potential ROI metrics before establishing specific process mapping, potentially overlooking optimization areas.

It examines broader workflows without focusing on individual steps, missing granular opportunities for automation.

It allows pinpointing specific steps or sub-tasks within a workflow that could be automated, improving efficiency and reducing errors.

It assumes mapping processes is sufficient to complete automation implementation without considering task dependencies or broader workflows.

NO.26 What is the main purpose of using a context grounding strategy with an ECS Index in Agents designer canvas in Studio Web?

To keep the ECS Index stored in a shared Orchestrator folder.

To define static rules for retrieving data from the index.

To limit the number of results retrieved from the ECS Index.

To retrieve data based on the user’s current session or inputs.

NO.27 You are building an agent that classifies incoming emails into one of three categories: Urgent, Normal, or Spam. You want to improve accuracy by using few-shot examples in a structured format. Which approach best supports this goal?

Include three random emails and let the LLM guess the intent.

Use unlabeled prompts followed by ranked categories:
Classify this. “Need update on report.” – [1] Urgent [2] Normal [3] Spam

Use examples such as:
Input: “Please address this issue immediately, server is down!” Output: “Urgent”

Show one example and leave the label blank for inference.

NO.28 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?

Change every field, including Channel Id, to Argument because an agent cannot infer any field values without explicit arguments.

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.

Declare the field as an output argument in Data Manager so the agent can feed a value back into the tool.

Change every field, including Channel Id, to Variable because an agent cannot infer any field values without explicit arguments.

NO.29 What type of agents can be invoked using the ‘Start and wait for external agent’ feature in UiPath Maestro?

Only UiPath Orchestrator robots.

External agents like Salesforce or ServiceNow.

Agents configured exclusively within the same project.

Agents that do not require any input or output variables.

NO.30 In a UiPath Agent, which statement best captures the essential purpose of a system prompt?

It declares the agent’s role, overall goal, and operating constraints, and tells the agent when to invoke tools or escalate tasks to a human reviewer.

It is used only to preload enterprise context and never influences the agent’s decision to call tools.

It mainly lists output-formatting tags the agent must include, leaving role and goal definition to the user prompt.

It must enumerate every possible dialogue path the agent could encounter so the model can simply pick a preset answer.

NO.31 Why is it important to include examples in prompts?

Including examples should only focus on edge cases while ignoring typical scenarios for better variety in results.

Examples should be omitted to allow the AI to create responses entirely from general knowledge without guidance.

Including examples guarantees output accuracy without any need for further adjustments or refinements.

Carefully chosen examples help guide the agent and improve its ability to generalize across different scenarios.

NO.32 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?

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.

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

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.

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

NO.33 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?

Options: List words like: “great, okay, bad” and map them to tone.

Input: “The app is okay I guess.” # Output:

“Text” Use a multiple-choice table with numerical ratings from 1-5.

Input: “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”

NO.34 An agent uses Web Search, Slack integration, and a custom process to resolve IT support tickets. The agent must:
* Retrieve relevant troubleshooting steps from the web.
* Notify the user via Slack if a solution is found.
* Escalate unresolved tickets via a custom process.
Which evaluation strategy ensures comprehensive coverage while avoiding redundancy?

Use random input sampling across all tools and rely on the default “LLM-as-a-Judge” assertion.

Create more than 30 evaluations for Slack notifications, more than 30 for web searches, and more than
30 for escalation processes.

Group evaluations into sets: Valid web results triggering Slack notifications, Invalid web results triggering escalations, Edge cases.

Create 30 evaluations for Slack notifications, 30 for web searches, and 30 for escalation processes.

NO.35 You are part of a Procurement team that often struggles with manually reviewing and comparing quotations from different vendors. This process is time-consuming, prone to human errors, and lacks real-time price validation. Keeping up with internal rules and market standards makes things even more difficult. This can cause problems and cost overruns. How agents can help?

Agents focus on sending reminders for deadlines but do not automate price analysis, extract item details, or validate compliance with internal rules, slowing down decision-making for procurement officers.

Agents rely on preloaded prices set by vendors and do not research market rates, verify compliance, or provide detailed validation, leading to potential errors and inefficiencies during quotation reviews.

Agents automate price validation by extracting item details from quotations, use tools to research market prices, checking policy compliance, and cross-verifying prices against benchmarks before sharing results with procurement officers for better decision-making.

Agents only store vendor quotations without cross-verifying prices, researching market trends, or checking compliance with policies, leaving procurement officers to manually manage the entire validation process.