UiPath-AAAv1 Exam Dumps - UiPath Certified Professional - General Track Questions and Answers

Bis correct — UiPath providesend-to-end configuration capabilitiesforContext Grounding, including:

Creating indexesin Orchestrator

Controlling accessviafolder-level permissions

Selecting LLMsfrom theLLM Gateway

Keeping indexesup to dateusing theUpdate Context Grounding Index activity

This allows agents to accessreal-time enterprise context, reducing hallucinations and enhancing accuracy when performing actions or generating responses.

Option A underestimates the feature scope.

C and D are incorrect — UiPath supportsautomated syncs, granular access control, andmulti-model compatibility.

UiPath’s platform treats grounding as agoverned, scalable enterprise feature, critical for AI safety and relevance.

Cis correct — agents in UiPath canintelligently automate complex procurement workflowsby combining tools likedocument extraction,web search for price benchmarks,policy validation, andLLM-based reasoning.

In this use case:

The agent extractsstructured data(item, price, quantity) from multiple quotations

Compares prices withexternal market sourcesusingWeb Searchor integrated APIs

Appliescompany policies or thresholdsusing system prompts and guardrails

Flags anomalies, escalates exceptions, or provides summarized comparisons

This reduces:

Manual effort

Human error

Turnaround time for approvals

And increases:

Policy compliance

Market alignment

Decision speed for procurement officers

Options A, B, and D all fall short of UiPath agent capabilities. These responses describepassive or limitedautomations, whereas agents are built to operateproactively and contextually, especially in high-value business functions like procurement.

This example reflects theagentic automation blueprintat work — combining perception, decision, and action across multiple systems in real time.

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.

Dis correct —Context Groundingin UiPath usessemantic search across indexed contentto provide relevant and meaningful context to the agent, even when the data spansmultiple documents.

This capability is powered by:

Embedding-based similarity search(e.g., cosine similarity)

Intelligent chunking and indexing of enterprise data

Runtime query matching based on theagent’s prompt or user input

This enables agents to:

Retrieverelevant information across distributed content

Detectrelationships between topics, even if data is fragmented

Supportmulti-document summarization,comparison, andknowledge-based reasoning

For example, an agent could compare policy details across multiple HR documents to generate a unified response or identify inconsistencies in invoice records spread across different files.

Option A is false —Context Grounding is automaticonce indexing is configured.

B is incorrect — it's explicitly designed toquery across documents.

C misrepresents the system — it doesn’t extract random text; it retrievessemantically relevantpassages based on the LLM's intent.

This powerful grounding mechanism makes UiPath agentsintelligent, context-aware, and enterprise-ready, especially in knowledge-intensive environments.

Comprehensive and Detailed Explanation (from UiPath Agentic Automation documentation):

The correct approach isC, as it best reflects thefew-shot prompting pattern, which is a well-documented and recommended technique in both UiPath Autopilot™ and broader agentic AI design for improvingintent classificationaccuracy.

InUiPath Agentic Automation, especially inPrompt Engineering, few-shot examples serve to "ground" the Large Language Model (LLM) with task-specific context. Providingstructured input-output pairs(as shown in option C) allows the model to learn from the context and mirror the expected output more reliably — enhancing classification precision.

For instance, UiPath recommends using clearly formatted training examples in this structure:

Input: "[Text]"

Output: "[Label]"

This aligns with UiPath’s guidance under thePrompt Engineering Framework, which highlights that usingfew-shot exemplars with clear task demonstrationsignificantly improves model performance over zero-shot or ambiguous input formats (as in options A or B). Option D also underperforms due to insufficient grounding.

UiPath emphasizes the importance oflabel clarity,format consistency, andexplicit instruction— all of which are satisfied in Option C. This method also supportspromptgeneralizationfor new inputs by modeling how categorization should happen, not just what categories exist.

This technique is crucial in real-world agentic workflows where LLMs handle noisy, unstructured data (like emails), and are expected to trigger appropriate downstream actions such as ticket creation, escalation, or filtering.

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.

Cis correct — according to UiPath documentation,key-value pairs stored in Agent Memorypersist for12 months by default.

Agent Memoryis a persistent storage layer allowing agents to:

Recall decisions or context across runs

Store user preferences, status, or temporary flags

Maintain statefulness without relying on external databases

This capability is especially useful for:

Omnichannel customer interactions

Preference-aware recommendations

Tracking previously taken actions for continuity

Although memory storage is long-lasting (12 months), developers can:

Manually resetor expire entries

Use different memory scopes (e.g., per-user, per-agent)

Design memory-aware flows for personalization

Option D is incorrect — memory isnot auto-cleared on version updates.

A and B understate the retention policy — default expiration is clearly documented as12 monthsunless changed manually.

Agent Memory is a powerful enabler ofcontext-rich, stateful automations, especially for conversational or ongoing interactions.

Dis correct — the defining feature offew-shot promptingis the inclusion ofmultiple input-output exampleswithin the prompt todemonstrate the desired behavior or output structureto the LLM.

In UiPath’s Agentic Prompting practices, few-shot examples help:

Anchor the model to a consistent format

Reduce ambiguity in task instructions

Improve performance in tasks like classification, transformation, or content generation

Example:

Input: “My password isn’t working.”

Output: “Category: Login Issue”

Input: “App won’t open.”

Output: “Category: Access Error”

This trains the model within the prompt — no fine-tuning required — making it apowerful design patternin building intelligent agents.

Option A describeschain-of-thought prompting.

B refers tozero-shot prompting.

C refers toprompt chaining, used in advanced orchestration, not few-shot logic.