UiPath UiPath Certified Professional - General Track UiPath-AAAv1 New Questions

The correct answer isD— theOutcome Behavior sectionis where you configure how the agent should respond once an escalation is resolved by a human.

In UiPath’sagent design process, when a task is escalated to a human reviewer (viaAction Center, for instance), the agent:

Waits for human input

Receives anOutcome(e.g., Approve, Reject, Flag)

Then continues its process based on logic defined in theOutcome Behavior

This may include:

Proceeding with the automation

Triggering an alternate flow

Logging results or escalating further

Other options are incorrect or refer to unrelated settings:

A (Assignment recipient list) defineswhogets the task — not what happens after.

B (Agent Memory toggle) governscontext retention, not post-escalation behavior.

C (Input descriptions) help users understand fields but don’t control flow logic.

TheOutcome Behavior sectionensures agents respondintelligently and consistently after human interaction, which is critical in hybrid workflows involving both automation and human-in-the-loop review.

Dis correct — inTask CaptureandBPMN modeling, theExclusive Gatewayis the BPMN element that represents adecision point. It is used to:

Split the process intomutually exclusive paths

Route flow based on acondition or choice

When mapping these processes toagent behavior, the Exclusive Gateway typically translates to a“Decision” node, where the agent:

Evaluates logic (e.g., “Is amount > $10,000?”)

Selects one path forward (e.g., “Escalate” vs. “Auto-approve”)

This is a fundamental construct in UiPath’sagentic process modeling, enabling agents to handlebranching logic, make contextual choices, or call different tools based on runtime data.

Option A (Task) represents an activity, not a decision.

B (Swimlane) is used to group actions by role — not functional logic.

C (User Task) represents human involvement — not branching conditions.

UiPath emphasizes decision modeling to make agentsadaptive and responsive, and Exclusive Gateways are the tool to model such decisions cleanly and visually.

Cis correct — UiPath’sContext Groundingsystem usescosine similarity searchto retrieve the most relevant documents, passages, or semantic chunks from indexed data during runtime.

This retrieval process is critical for:

Minimizing hallucination

Enhancingprompt grounding

Connecting agents toreal-time enterprise knowledge

Here’s how it works:

All documents areembedded into vector spaceusing a model (e.g., OpenAI or Azure OpenAI embeddings)

At runtime, the agent’s query isconverted into a vector

Acosine similaritycomparison is used to fetch themost semantically similar documentsbased onangle (not magnitude)between vectors

This enables agents to provideaccurate, contextual answers, like referencing HR policies or IT guides.

Other options:

A (Softmax) is a classifier, not a search method

B (Sigmoid) is for binary classification

D (ReLU) is an activation function in neural nets — not for search

Bis correct — for tasks involvingtone, style, brand alignment, and personalization,model-graded evaluationis the best choice.

UiPath’sagent evaluation frameworksupports multiple types of evaluation:

Model-graded: LLMs score or classify outputs based on nuanced criteria (e.g., tone match, relevance)

Human-graded: For subjective tasks

Deterministic: For strict accuracy checks (e.g., regex, classification)

In creative tasks likeemail drafting, deterministic methods (D) or length-based metrics (A)fail to capture nuance.

A/B testing (C) is useful in live experiments, but not for structured evaluation during development.

Model-graded evaluations enablescalable quality checksfor outputs that mustfeel human, on-brand, and context-aware— essential for personalized communication.