Be able to diagnose and fix GenAI infrastructure issues in Azure: scale model deployments to stop throttling, choose Entra ID or managed identity authentication for AI Foundry projects, run built-in evaluations, and route model access logs to Azure Monitor for auditing.
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Genaiops Infrastructure — choose a session length
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Domain overview
This domain covers the infrastructure that keeps GenAI workloads running on Azure: provisioning and scaling model deployments, authenticating to Azure AI Foundry projects, monitoring and evaluating deployed models, and auditing model access across an organization. Questions are scenario-based, asking you to pick the most effective Azure-native fix for throttling, access, evaluation, or compliance problems.
Exam objectives
Scaling model-as-a-service deployments and handling throttling with Azure AI Foundry quota and capacity settings
Authenticating to Azure AI Foundry projects using Microsoft Entra ID, managed identities, and API keys
Using Azure AI Foundry evaluation features to measure model performance with built-in metrics
Auditing model access logs with Azure Monitor, diagnostic settings, and Log Analytics workspaces
Assuming throttling is fixed by retrying or prompt changes instead of increasing deployment capacity or quota in Azure AI Foundry.
Treating API keys as the preferred auth method when Microsoft Entra ID and managed identities are the recommended, more secure options.
Believing evaluation requires external tooling rather than Azure AI Foundry's built-in evaluators and metrics.
Click any question to see the full explanation and answer options, or start a focused practice session above.
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?
2Your 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?
3You need to audit all model access logs across your organization. What is the most effective approach?
4You 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?
5You need to ensure that your LLM responses are filtered for harmful content. Which infrastructure component provides this capability?
6An agent orchestration system is timing out because it waits too long for tool output. How can you optimize the infrastructure handling of tool calls?
7Where do you define the model deployment settings, including instance count and version?
8You 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?
9Your team wants to use prompt versioning. Where should this be managed in the AI Foundry workflow?
10You notice your model deployment status is 'Succeeded' but inference requests are failing. Where should you first check for connectivity issues?
11What is the recommended approach for managing configuration settings for different environments (Dev, Test, Prod) in an AI application?
12What is the benefit of using 'Model Catalog' in Azure AI Foundry?
13You 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?
14Which Azure feature should you use to restrict traffic to your AI Foundry project to specific IP addresses?
15You have a sudden spike in requests. What is the quickest way to check if you have hit your Azure OpenAI token quota?
16You are automating the deployment of your AI project. What is the recommended tool to manage infrastructure-as-code (IaC) for Azure AI Foundry?
17What is the purpose of a 'System-Assigned Managed Identity' in the context of an Azure AI project?
18You are deploying a large model. During the deployment, you encounter a 'Resource Not Available' error. What is the most likely cause?
19You want to automate the testing of your prompt flows as part of your CI/CD pipeline. Which tool should you use?
20When sharing an AI project with a team, which resource group architecture is best practice for lifecycle management?
21You are building a RAG application. Where should you store the indexed documents for optimal retrieval performance?
22Which Azure AI Foundry feature allows you to evaluate your model's performance?
23You need to monitor the 'latency' and 'token usage' of your model deployments in real-time. Which tool provides the most granular view?
24What is the primary function of the 'Prompt Flow' tool within Azure AI Foundry?
25You are configuring a connection to a vector store. What is the best way to handle the secret credential?
26You need to update a prompt flow without downtime. What deployment strategy should you use?
27Which TWO of the following are valid ways to authenticate to an Azure AI Foundry project?
28Which THREE of the following are components of an Azure AI Foundry project?
29You are planning a production GenAI deployment. Which THREE of the following are necessary infrastructure considerations?
30You are deploying a model that requires specific GPUs. How do you ensure you get the right infrastructure?
31You are troubleshooting a failed agent deployment. Which TWO areas should you inspect first?
32Your team wants to monitor the safety of model outputs. Which THREE steps should you take?
33You want to automate the scaling of your AI agent orchestration. Which TWO components do you need to configure?
34To optimize the cost of your AI infrastructure, which THREE actions should you consider?
35Which THREE items should you keep track of in your AI Foundry project to ensure good governance?
36You are experiencing throttling on your AI endpoint. Which TWO steps should you take?
37Which THREE features does Azure AI Foundry provide to help developers?
38Which THREE metrics are critical for monitoring the health of a GenAI deployment?
39Which TWO of the following are benefits of using Prompt Flow for orchestration?
40A team is scaling a GenAI application that utilizes multiple models via model-as-a-service. You notice that inference requests are being throttled. What is the most effective infrastructure-level adjustment to handle the increased load?
41You are setting up a CI/CD pipeline for GenAI models. You need to evaluate the model's performance on a benchmark dataset before it is promoted to the production endpoint. Which Azure AI Foundry capability should be integrated into your pipeline?
42You are designing the infrastructure for a multi-agent system. Which THREE of the following are essential components to ensure agent orchestration scalability?
43A developer is troubleshooting an agent that is failing to connect to an external tool. Which THREE of the following infrastructure settings should be verified?
44Your team runs a GenAI agent in Azure AI Foundry that calls an Azure OpenAI model deployment. The agent intermittently fails with authentication errors, and you suspect the managed identity used by the agent's compute does not have the required role assignment on the Azure AI Foundry resource. You need to verify and fix the role assignment. Which action should you take?
45You manage a GenAI application in Azure AI Foundry that uses a provisioned throughput deployment for a large language model. Finance asks you to reduce cost during off-peak hours without affecting peak-hour performance. The deployment currently runs at a fixed provisioned capacity around the clock. Which approach should you use?
Be able to diagnose and fix GenAI infrastructure issues in Azure: scale model deployments to stop throttling, choose Entra ID or managed identity authentication for AI Foundry projects, run built-in evaluations, and route model access logs to Azure Monitor for auditing.
The Courseiva AI-300 question bank contains 45 questions in the Genaiops Infrastructure domain. Click any question to see the full explanation and answer breakdown.
Start with a 10-question focused session to identify your baseline accuracy in this domain. Read every explanation — even for questions you answer correctly — to understand the reasoning. Once you score consistently above 80%, move to a 20–30 question session to confirm depth before moving to the next domain.
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