Pass the UiPath UiPath Certified Professional - General Track UiPath-AAAv1 Questions and answers with CertsForce

Cis correct — mapping processes during agentic discovery is essential because it allows teams tozoom into specific tasks or sub-processeswhere agentic automation can deliver the highest value.

UiPath’sAgentic Design Blueprintmethodology emphasizes this as afoundational step. By creating detailed "as-is" process maps, teams can:

Spotrepetitive tasks(ideal for RPA)

Findjudgment-based decisions(ideal for agents)

Highlightescalation points, delays, and handoffs

This clarity helps identify:

Which actions can be automated

Which roles require agent augmentation

What context (data or documents) is needed

Option A skips process mapping and risks missing real value.

B is too high-level — real insights come from step-level granularity.

D is misleading — mapping is necessary butnot sufficientfor full implementation.

Accurate process mapping creates avisual and logical foundationfor designing agents that integrate seamlessly into workflows — targeting the right problems and unlocking measurable ROI.

The correct answer isD— according to UiPath’sMaestro orchestration framework, theprocess ownerplays a central role in defining and governing agentic workflows.

In UiPath Maestro:

Process ownersuseBPMN diagramsto map the flow of work, decision points, hand-offs, and automation steps.

They defineagent boundaries, escalation rules, and success conditions.

This model empowersbusiness-side expertsto own automation design while working alongside technical teams.

Unlike classic automation that’s owned by IT or CoE developers, agentic processes requirebusiness-context awareness, makingprocess ownersessential to managing thefull lifecycle— from design to governance to optimization.

Options A and B refer to support roles. Option C (developers) implement parts of the design, but don’t usually govern the lifecycle or own the process vision.

This reflects UiPath’s broader push forbusiness-led automation, enabled by Maestro and Autopilot™ in Studio Web.

The correct answer isA— UiPath’sagentic orchestration layerusesBPMN (Business Process Model and Notation)to visually model and govern the workflows in which AI agents operate. This is a core feature ofUiPath Maestro, where BPMN ensures:

Clear definition of rules, handoffs, and agent actions

Guardrails for decision-making

Coordination between people, robots, and AI agents

Reusability and governanceof business logic

Agentic orchestration doesnot mean giving full autonomy to agents(as in D), nor does it aim to eliminate human input entirely (as in B). Instead, it promotesadaptive workflowswhere human review, agent action, and automation co-exist in a governed way.

Option C is incorrect because UiPath specificallyencourages hybrid collaborationbetween humans, bots, and agents. BPMN is the bridge that brings that orchestration to life.

Cis correct — useWeb Searchin an agent workflow when you need the LLM toquery public internet sources(e.g., news, pricing, documentation), butdon’t have a specific URL.

UiPath Autopilot and Agentic Agents distinguish:

Web Search: For open-ended discovery from the web (e.g., "find latest refund policies from airlines")

Web Reader: For extracting or summarizing content from aspecific, known URLor internal portal

Web Search is ideal for:

Aggregating public info

Real-time summaries

Context retrieval for grounding the prompt

A and B involveinternal sources— use tools likeKnowledge RetrievalorAPI connectorsinstead.

D calls fortargeted extraction, better suited toWeb Readerwith structured parsing.

Bis correct — when a field (likeChannel Id) is set toPrompt, the agent will attempt to infer its valueat runtime, based on theinstructions in the promptand the context provided.

This is the default and preferred mode for agent tools when:

The agent has enough context or memory to decide

You wantLLM autonomyin filling the field dynamically

You're using prompt instructions like: “Post to the user’s default Slack channel”

Option A is incorrect — “Argument” is used when you're passing aspecific variableinto the agent prompt (not inferred).

C misunderstands data flow direction — “Output” is not relevant for input fields.

D is invalid — “Variable” is not the standard method for field inference in this scenario.

This aligns with UiPath’sagent + tools orchestrationmodel usingStudio Web’s low-code agent builder.

Bis the correct answer — in UiPath's Agent Builder (Studio Web),descriptions for input and output arguments serve as grounding contextfor the agent. These descriptions help the LLMunderstand what each argument represents, how it should be used in the generation process, and how to structure its outputs.

This is especially critical for:

Inputs like {{CUSTOMER_ISSUE}} — the agent needs to know it’s a complaint, question, or error

Outputs like {{TROUBLESHOOTING_STEPS}} — the agent should format these as steps, not just a summary

These descriptions:

Improve theaccuracy of prompt generation

Ensure the agentreturns structured, expected data

Help guide LLM behavior in multi-step or dynamic workflows

Option A is incorrect — Orchestrator triggers donot auto-mapbased on descriptions.

C is false — descriptions donot make arguments mandatory.

D is incorrect —output arguments benefit greatly from descriptions, especially for guiding LLMs on return format and content.

Dis correct — includingwell-designed examplesin prompts is a key part offew-shot learning, which helps LLM-based agents better understand thetask structure, output style, and expected behavior.

UiPath encourages the use of examples for:

Classification(e.g., labeling sentiment, email categories)

Transformation tasks(e.g., turning unstructured text into tables)

Step-by-step instructions(e.g., troubleshooting flows)

These examples serve two purposes:

Pattern induction: The model picks up on consistent structures or rules used across examples.

Generalization: With diverse examples, the agent can apply logic to unseen but similar cases.

Best practice:

Usetypical, real-world examplesrepresentative of the data the agent will encounter.

Keep formatsclear and consistentacross input-output pairs.

Pair examples withexplicit instructionsin the system or user prompt.

Option A is flawed — focusing only on edge cases can confuse the model.

B is false — omitting examples forces the LLM to guess the structure, reducing accuracy.

C is misleading — examples improve performance butdo not guarantee perfect output; testing and evaluation are still required.

In short,prompt engineering with examples is essentialto buildingreliable, generalizable, and scalableAI agents.

Cis correct — LLMs like those used in UiPath’s Agentic Automation rely heavily ontokenization, which breaks input text into subword units (tokens). When complex technical terms (e.g., “UiPath.Orchestrator.API”) aresplit across multiple tokens, the model may not interpret themconsistently or accurately, especially if:

They're rare or domain-specific

Appear in different token contexts

Are inconsistently represented in training data

This is a common challenge in fine-tuning LLMs fortechnical documentation, where small changes in tokenization can shift meaning or relevance weighting. It’s why UiPath emphasizesprompt engineeringandcontext groundingto mitigate misinterpretation.

A is incorrect because thetoken limitaffects response length, not term understanding.

B is misleading — frequency matters, butsemantic relationshipsalso influence interpretation.

D is factually wrong — LLMs absolutely rely on tokenization and arenot rule-basedwith pre-programmed definitions.

Understanding how tokenization impacts prompt fidelity is critical when building agents that use LLMs to generatestep-by-step or technical outputs.