AI-103 Implement Generative AI And Agentic Solutions Practice Test 11 — 15 Questions

Question 1

You notice your Azure AI agent is producing offensive content. You need to configure a guardrail that intercepts the model output before it is sent to the user. Which service should you integrate?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the standard guardrail for filtering model output.

Answer

Azure AI Search Filters

Answer

Prompt Template Validation

Answer

Azure Monitor

Question 2

Which component in Azure AI Foundry allows you to visualize and debug the execution path of a model call, including input/output mapping?

Accepted Answer

Prompt Flow. Prompt Flow provides the visual designer and debugger for workflows.

Answer

Compute Instance

Answer

Model Benchmarks

Answer

Deployment Endpoint

Question 3

You are building a RAG solution and observe that the model retrieves the correct documents but fails to use the information accurately. What is the most effective approach to improve performance?

Accepted Answer

Refine the system prompt instructions. Iterative prompt engineering and improving the system message instructions are key to improving RAG performance.

Answer

Increase the vector index size

Answer

Use a smaller base model

Answer

Disable streaming response

Question 4

Which Azure AI Foundry feature helps identify drift in model performance after deployment?

Accepted Answer

Model Monitoring. Model monitoring tracks performance metrics over time to identify drift.

Answer

Data Drift Analysis via Compute

Answer

Prompt Flow Tracing

Answer

Model Catalog

Question 5

You are automating document ingestion for a RAG pipeline. Which feature allows you to automatically split large documents into smaller chunks during indexing?

Accepted Answer

Indexers with Skillsets. Azure AI Search indexers support skillsets for splitting and processing documents.

Answer

Prompt Flow Vector Store Lookup

Answer

Model Fine-tuning

Answer

Vector Embeddings API

Question 6

In Azure AI Foundry, which tool would you use to measure the quality of your RAG application against a golden dataset?

Accepted Answer

Evaluation. Evaluation in Azure AI Foundry uses metrics to assess RAG pipelines.

Answer

Model Catalog

Answer

Deployment

Answer

Prompt Flow Tracing

Question 7

You are deploying an LLM in Azure AI Foundry and want to restrict access to the endpoint to a specific virtual network. What should you configure?

Accepted Answer

Private Endpoint. Private Endpoints are used to secure access to Azure services within a VNet.

Answer

Azure AD Conditional Access

Answer

Firewall Rule on the Model

Answer

Managed Identity

Question 8

You need to ensure that an LLM-based agent in your application follows a specific output schema (JSON) for downstream processing. What is the most reliable way to enforce this?

Accepted Answer

Configure the response_format property to JSON. Structured output (Response Format) parameters ensure the model adheres to a specific schema.

Answer

Use a few-shot prompt with JSON examples

Answer

Use a custom parser in Prompt Flow

Answer

Set the Stop Sequences parameter

Question 9

When sharing a Prompt Flow with a teammate, which action allows them to run the flow without having to recreate the connections?

Accepted Answer

Grant permissions to the shared Connection resource. Sharing the project and ensuring appropriate RBAC or shared connection access allows collaboration.

Answer

Delete the connection and recreate it

Answer

Hardcode credentials into the flow

Answer

Export the flow as a text file

Question 10

What is the primary benefit of using the Model Catalog in Azure AI Foundry?

Accepted Answer

Centralized discovery and deployment of models. The Model Catalog provides a centralized location to discover and deploy models.

Answer

Automated prompt engineering

Answer

Network security configuration

Answer

Automatic data labeling

Question 11

You are performing Retrieval-Augmented Generation. You need to ensure the retrieved data includes metadata for filtering. Where should this metadata be defined?

Accepted Answer

Azure AI Search Index Fields. Metadata is defined in the Azure AI Search index schema.

Answer

Environment variables

Answer

Prompt Flow input schema

Answer

System message content

Question 12

Which TWO of the following are valid methods to improve the quality of retrieved data in a RAG system?

Accepted Answer

Implementing a hybrid search strategy. Better chunking strategies and hybrid search improve retrieval quality.

Answer

Reducing the number of vector dimensions

Answer

Changing the chat history session length

Answer

Increasing the number of tokens in the prompt

Question 13

Which THREE of the following are components of a standard Prompt Flow DAG (Directed Acyclic Graph)?

Accepted Answer

Inputs. Inputs, Tools (Nodes), and Outputs are fundamental to Prompt Flow structure.

Answer

Model Training Jobs

Answer

Data Labeling Projects

Question 14

Which TWO criteria are typically used to evaluate the grounding of an LLM-based application?

Accepted Answer

Relevance. Groundedness and relevance are core metrics in Azure AI Foundry evaluation.

Answer

Latency

Answer

Token cost

Answer

Model version number

Question 15

Which THREE of the following are best practices for Responsible AI when using Generative AI solutions?

Accepted Answer

Implementing content safety filters. Transparency, safety filtering, and human oversight are essential pillars.

Answer

Removing all system instructions to maximize creativity

Answer

Collecting all user PII for model training