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UiPath UiPath-AAAv1 Exam Syllabus Topics:

UiPath Certified Professional Agentic Automation Associate (UiAAA) Sample Questions:

1. Which similarity search function is leveraged when Context Grounding is used by UiPath Products like Agents?

A) ReLu similarity search
B) Sigmoid similarity search
C) Softmax similarity search
D) Cosine similarity search

2. What are the primary benefits of Context Grounding when querying data across multiple documents?

A) Context Grounding requires manual intervention for identifying connections between data points across documents.
B) Context Grounding only extracts random sentences without contextual understanding.
C) Context Grounding is limited to querying within a single document at a time.
D) Context Grounding understands relationships between data points across documents, enabling tasks like summarization, data comparison, and retrieval of highly relevant information.

3. 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?

A) Use examples such as:
Input: "Please address this issue immediately, server is down!" Output: "Urgent"
B) Show one example and leave the label blank for inference.
C) Include three random emails and let the LLM guess the intent.
D) Use unlabeled prompts followed by ranked categories:
Classify this. "Need update on report." - [1] Urgent [2] Normal [3] Spam

4. How does adjusting the "Number of results" setting affect the agent's use of context from indexes?

A) It selects which Orchestrator folder to use, determining the location of stored workflows and deciding which set of predefined rules will apply during data retrieval and processing.
B) It modifies the similarity threshold for chunk retrieval and lowers the number of tokens used.
C) It changes the number of chunks returned, impacting both the size of the grounding payload and the filtering of relevant information.
D) It makes the agent ignore all context completely, resulting in outputs that are entirely disconnected from the indexed data, regardless of its relevance to the query or prompt provided.

5. A developer is implementing a few-shot structured prompt for an email classification task. The prompt includes examples of email subjects labeled with their respective classifications, such as "Spam" or "Work." What is the most important aspect to consider when selecting examples for the prompt?

A) Include examples with intentionally incorrect labels to improve training.
B) Always use more than 10 examples, regardless of task complexity.
C) Choose examples that are diverse, relevant, and typical of the task's expected input.
D) Use random and unrelated examples to test the prompt's robustness.

Solutions: