AI-300 Genaiops Infrastructure Practice Test 2 — 15 Questions

Question 1

What is the recommended approach for managing configuration settings for different environments (Dev, Test, Prod) in an AI application?

Accepted Answer

Use 'App Configuration' service to manage environment-specific variables.. Using environment variables or app configuration services is the standard way to inject configuration without hardcoding.

Answer

Deploy different model versions for each environment.

Answer

Create separate Azure subscriptions for every environment.

Answer

Hardcode values in the application code.

Question 2

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

Accepted Answer

It provides pre-optimized models with one-click deployment.. The Model Catalog provides a curated list of models that are pre-optimized for deployment on Azure infrastructure.

Answer

It provides free LLM usage for all models.

Answer

It eliminates the need for authentication.

Answer

It automatically builds your application UI.

Question 3

You are troubleshooting high latency in a RAG-based application. The vector search is fast, but the generation phase is slow. Which component should be scaled?

Accepted Answer

Increase the 'Instances' count for the LLM deployment.. If the generation phase is slow, you likely need to scale the LLM inference endpoint (e.g., increasing instances or provisioned capacity).

Answer

Optimize the vector database index.

Answer

Reduce the 'temperature' setting.

Answer

Increase the 'Embedding Model' throughput.

Question 4

Which Azure feature should you use to restrict traffic to your AI Foundry project to specific IP addresses?

Accepted Answer

Azure AI Services 'Networking' (Firewall) settings. IP Firewall rules in the Azure AI Services resource allow you to define allowed client IP ranges.

Answer

Azure Active Directory Conditional Access

Answer

Network Security Groups (NSG) on the VM

Answer

Azure Front Door WAF

Question 5

You have a sudden spike in requests. What is the quickest way to check if you have hit your Azure OpenAI token quota?

Accepted Answer

Check the 'Quotas' tab in Azure AI Foundry.. The 'Quotas' section in the Azure AI Foundry or the Azure Portal 'Usage + Quotas' page displays current usage versus limits.

Answer

Review your monthly invoice.

Answer

Check the Azure Monitor 'Errors' log.

Answer

Check the model endpoint deployment logs.

Question 6

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 7

What is the purpose of a 'System-Assigned Managed Identity' in the context of an Azure AI project?

Accepted Answer

To provide a secure identity for the resource to interact with other Azure services.. Managed identities allow the project or compute resource to securely access other Azure resources (like Key Vault or storage) without managing explicit credentials.

Answer

To automatically encrypt all data in transit.

Answer

To allow users to log in to the project portal.

Answer

To provide public IP connectivity.

Question 8

You are deploying a large model. During the deployment, you encounter a 'Resource Not Available' error. What is the most likely cause?

Accepted Answer

The region does not have capacity for the requested model SKU.. This error usually indicates that the region lacks sufficient capacity for the specific SKU requested.

Answer

The API key is invalid.

Answer

The project name is too long.

Answer

The subscription is not registered for the service.

Question 9

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

Question 10

When sharing an AI project with a team, which resource group architecture is best practice for lifecycle management?

Accepted Answer

Keep the project and its dependencies in a single resource group.. Grouping the project, storage, and key vault in a single resource group simplifies management and deletion of the entire lifecycle.

Answer

Use a public resource group for all team members.

Answer

Spread resources across multiple regions.

Answer

Place each component in a different subscription.

Question 11

You are building a RAG application. Where should you store the indexed documents for optimal retrieval performance?

Accepted Answer

Azure AI Search. Azure AI Search is the native indexer and retriever for AI Foundry RAG applications.

Answer

Azure Blob Storage

Answer

Azure Cache for Redis

Answer

Azure SQL Database

Question 12

Which Azure AI Foundry feature allows you to evaluate your model's performance?

Accepted Answer

Evaluation. Evaluation in Azure AI Foundry allows you to run metrics against your model outputs to assess quality.

Answer

Endpoint monitoring

Answer

Model Catalog

Answer

Deployment logs

Question 13

You need to monitor the 'latency' and 'token usage' of your model deployments in real-time. Which tool provides the most granular view?

Accepted Answer

Azure Monitor with diagnostic logs. Azure Monitor with custom log queries against the diagnostic logs of the AI resource provides the most granular view.

Answer

Azure Billing dashboard

Answer

Azure Activity Log

Answer

Azure AI Foundry 'Overview' tab

Question 14

You have a new model deployment that is incurring high costs. How can you find the top-consuming deployments?

Accepted Answer

Use 'Cost Analysis' in the Azure Portal.. The cost analysis tool in the Azure portal allows you to group costs by 'Resource' or 'Tag' to identify top-consuming deployments.

Answer

Check the 'Activity Log'.

Answer

Check the Azure AI Foundry 'Deployments' page.

Answer

Review the 'Quotas' tab.

Question 15

What is the primary function of the 'Prompt Flow' tool within Azure AI Foundry?

Accepted Answer

To create and test prompt-based workflows.. Prompt Flow is a development tool designed to streamline the entire development cycle of AI applications, from prototyping to deployment.

Answer

To manage Azure subscriptions.

Answer

To perform data visualization.

Answer

To manage firewall rules.