AI-300 Genaiops Infrastructure — 30 Questions

Question 1

To optimize token throughput for a high-traffic chatbot, you decide to enable 'Provisioned Throughput' for your Azure OpenAI deployment. What must you consider?

Accepted Answer

Use 'Azure Monitor' to view the 'Tokens Per Minute' metric.. Provisioned Throughput Units (PTUs) provide dedicated capacity, but they require a commitment to a specific throughput level which impacts cost and availability.

Answer

It can only be used with models deployed in the 'East US' region.

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It automatically scales the number of tokens based on CPU usage.

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Check the 'Quota' tab in the Azure Portal.

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Enable 'Diagnostic Settings' for the Azure AI resource.

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Query the 'ModelUsage' table in Log Analytics.

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It is incompatible with 'Streaming' requests.

Question 2

You are managing a multi-region GenAI deployment using Azure AI Foundry. To reduce latency for global users, what routing architecture should you implement?

Accepted Answer

Use Azure Front Door to route traffic to the nearest regional AI Foundry endpoint.. Azure Front Door provides global HTTP load balancing and site acceleration, which is critical for reducing latency in geographically dispersed GenAI applications.

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Implement 'Private Link' for every regional endpoint.

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Deploy Azure Traffic Manager with 'Performance' routing.

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Configure 'Global VNet Peering' between model endpoints.

Question 3

You are monitoring the cost of your GenAI infrastructure. You want to track usage per specific project. Which feature in Azure AI Foundry should you use?

Accepted Answer

Use 'Resource Tags' on the Azure AI project resource.. Tags are the standard mechanism in Azure for grouping resources and tracking costs associated with specific projects or teams.

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Configure 'Cost Analysis' alerts in the Azure Portal.

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Implement 'Azure Policy' to restrict resource types.

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Switch to 'Pay-As-You-Go' subscription model.

Question 4

Your production agent application requires strict data isolation. You must ensure all requests to Azure OpenAI are routed through a private network. Which configuration is required?

Accepted Answer

Enable 'Public Network Access' set to 'Disabled' on the Azure AI resource.. To ensure traffic stays within the private network, you must configure Azure AI Services with Private Link and ensure all traffic is routed through a private endpoint.

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Deploy the model inside a dedicated 'Azure Kubernetes Service' node pool.

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Configure a 'Service Tag' for Azure OpenAI in the Firewall settings.

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Add the 'Virtual Network' to the 'Allowed Subnets' in the AI Service.

Question 5

Your team is using Semantic Kernel for agent orchestration. You notice frequent transient errors when the agent interacts with multiple LLM services. How should you implement infrastructure-level resiliency?

Accepted Answer

Use an 'Azure API Management' policy to implement a retry pattern.. Implementing exponential backoff and retries via the 'RetryHandler' in the HttpClient factory or Semantic Kernel settings is the standard approach for infrastructure-level resiliency.

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Disable the 'Streaming' flag in the agent request.

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Switch the model to a higher tier instance type.

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Increase the 'Timeout' parameter in the model deployment manifest.

Question 6

You need to audit all model access logs across your organization. What is the most effective approach?

Accepted Answer

Enable 'Diagnostic Settings' to send logs to a 'Log Analytics Workspace'.. Enabling diagnostic settings on the Azure AI Services resource to send logs to a Log Analytics workspace is the standard for organization-wide auditing.

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Review individual request headers in the app logs.

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Use 'Azure Advisor' to detect unauthorized access.

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Download the 'Model Usage' report from the billing console.

Question 7

You are implementing an agentic workflow using Azure AI Foundry. The agent needs to access a secure external API. How should you store the API key securely within the project?

Accepted Answer

Store the key in an 'Azure Key Vault' and reference it as a 'Connection' in the AI Project.. Azure AI Foundry provides a 'Connections' feature that allows storing credentials as secrets, which can then be referenced by agents.

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Pass the key as an environment variable in the deployment YAML.

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Hardcode the key in the prompt template.

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Save the key in a local '.env' file in the code repository.

Question 8

You are deploying a high-throughput Large Language Model on Azure AI Foundry. You need to ensure the underlying infrastructure supports rapid auto-scaling based on request latency. Which configuration should you prioritize in the deployment settings?

Accepted Answer

Configure the 'Scale' settings in the deployment to use a custom metric based on 'Request Latency'.. Auto-scaling for Azure AI Model Catalog deployments is managed via the 'Scale' tab, specifically targeting 'Instances' to handle request concurrency.

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Enable 'Load Balancing' in the Azure AI Search resource.

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Increase the 'Quota' of the underlying Azure subscription.

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Set the minimum instance count to 0 in the deployment configuration.

Question 9

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

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Azure Key Vault

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Azure Bastion

Question 10

An agent orchestration system is timing out because it waits too long for tool output. How can you optimize the infrastructure handling of tool calls?

Accepted Answer

Implement 'Asynchronous' execution patterns for long-running tools.. Implementing tool-specific timeouts within the agent definition helps prevent long-running tasks from stalling the overall orchestration chain.

Answer

Increase the 'CPU count' on the hosting server.

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Increase the 'Global Timeout' for the entire agent.

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Reduce the 'System Prompt' size.

Question 11

Where do you define the model deployment settings, including instance count and version?

Accepted Answer

Azure AI Foundry -> Project -> Deployments. The 'Models + Endpoints' section within the Azure AI Foundry portal is where model deployments are managed.

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Azure Portal -> Subscriptions

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Azure Key Vault -> Secrets

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Azure Monitor -> Alerts

Question 12

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.

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Owner

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Reader

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Contributor

Question 13

Your team wants to use prompt versioning. Where should this be managed in the AI Foundry workflow?

Accepted Answer

Prompt Flow in Azure AI Foundry. The 'Prompt Flow' feature in Azure AI Foundry includes built-in versioning and management for prompt templates.

Answer

Azure Database for PostgreSQL

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GitHub repository only

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Azure Blob Storage

Question 14

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

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Azure Subscription usage reports

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Azure Storage Account logs

Question 15

You are deploying a custom model in a container. To ensure the model infrastructure is highly available, what is the best practice?

Accepted Answer

Deploy to 'Azure Kubernetes Service' with 'Availability Zones' enabled.. Deploying across multiple Availability Zones ensures that the infrastructure remains operational even if a specific datacenter fails.

Answer

Disable the 'Load Balancer' to save costs.

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Use a 'Standard' tier App Service plan.

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Deploy the container on a single large VM.

Question 16

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.

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Hardcode values in the application code.

Question 17

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.

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It provides free LLM usage for all models.

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It eliminates the need for authentication.

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It automatically builds your application UI.

Question 18

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.

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Reduce the 'temperature' setting.

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Increase the 'Embedding Model' throughput.

Question 19

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.

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Azure Active Directory Conditional Access

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Network Security Groups (NSG) on the VM

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Azure Front Door WAF

Question 20

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.

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Check the model endpoint deployment logs.