AI-300 Practice Test 4 — 10 Questions

Question 1

You need to ensure that your LLM responses are filtered for harmful content. Which infrastructure component provides this capability?

Accepted Answer

Azure AI Content Safety. Azure AI Content Safety is the integrated service used within AI Foundry to monitor and filter model outputs.

Answer

Azure Monitor

Answer

Azure Key Vault

Answer

Azure Bastion

Question 2

You are automating the deployment of your AI project. What is the recommended tool to manage infrastructure-as-code (IaC) for Azure AI Foundry?

Accepted Answer

Azure Bicep. Bicep is the native, recommended IaC tool for Azure, providing deep integration for AI Foundry resources.

Answer

Azure DevOps pipelines

Answer

Azure CLI alone

Answer

Python script with Azure SDK

Question 3

You are onboarding a team to Azure AI Foundry. You need to assign the minimum permissions for a developer to deploy and test a model. Which role should you assign?

Accepted Answer

Azure AI Developer. The 'Azure AI Developer' role provides sufficient permissions to manage resources and perform model deployments within the project.

Answer

Owner

Answer

Reader

Answer

Contributor

Question 4

You notice your model deployment status is 'Succeeded' but inference requests are failing. Where should you first check for connectivity issues?

Accepted Answer

The 'Endpoints' tab in Azure AI Foundry. The 'Endpoints' view in the AI Foundry project shows the connectivity status and endpoint URLs for the model.

Answer

The 'Keys' tab in the Azure Portal

Answer

Azure Subscription usage reports

Answer

Azure Storage Account logs

Question 5

Which TWO of the following are valid ways to authenticate to an Azure AI Foundry project?

Accepted Answer

API Keys.. Entra ID (RBAC) and API Keys are the two primary methods for securing access to AI resources.

Answer

Hardcoded usernames in the code.

Answer

Local file-based passwords.

Answer

Public IP allow-listing only.

Question 6

Which TWO of the following are benefits of using Prompt Flow for orchestration?

Accepted Answer

Integrated testing and evaluation.. Prompt Flow offers a visual interface for complex flows and integrated evaluation, which simplifies orchestration.

Answer

Removal of the need for an LLM.

Answer

No need for authentication.

Answer

Automatic hardware management.

Question 7

Which TWO metrics are most useful for evaluating the 'User Experience' in a conversational agent?

Accepted Answer

Relevance. Latency and the quality of the answer (Relevance) are the primary drivers of user satisfaction.

Answer

Disk I/O speed

Answer

Model training loss

Answer

GPU voltage

Question 8

You are planning a production GenAI deployment. Which THREE of the following are necessary infrastructure considerations?

Accepted Answer

Implementing Private Link for network isolation.. Security, scalability, and monitoring are the three pillars of a production-ready AI infrastructure.

Answer

Using the cheapest SKU regardless of capacity.

Answer

Hardcoding credentials in the code.

Question 9

You are using MLflow to track experiments in Azure Machine Learning. You need to log a custom metric that is calculated every 100 iterations. Which MLflow function should you use?

Accepted Answer

'mlflow.log_metric()'.. The 'mlflow.log_metric' function is used to log key-value pairs of metrics, which can be called within the training loop.

Answer

'mlflow.set_tag()'.

Answer

'mlflow.log_artifact()'.

Answer

'mlflow.log_param()'.

Question 10

You want to automate the testing of your prompt flows as part of your CI/CD pipeline. Which tool should you use?

Accepted Answer

Prompt Flow CLI. The 'pf' (Prompt Flow) CLI is designed to run flows and evaluations programmatically in CI/CD pipelines.

Answer

Azure Logic Apps

Answer

Azure Functions

Answer

Azure AI Search test console