AI-300 Genaiops Infrastructure Practice Test 3 — 15 Questions

Question 1

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

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Azure CLI alone

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Python script with Azure SDK

Question 2

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.

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To allow users to log in to the project portal.

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To provide public IP connectivity.

Question 3

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 4

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

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Azure Functions

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Azure AI Search test console

Question 5

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.. Keeping an AI project and its dependencies (model deployments, prompt flows, connections, storage, and search resources) in a single resource group is best practice because a resource group is the unit of lifecycle management in Azure. Deleting or exporting the resource group removes or captures the entire project atomically, RBAC and cost tracking are simplified, and deployment templates can be scoped to that group. This aligns resource boundaries with project boundaries.

Answer

Use a public resource group for all team members.

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Spread resources across multiple regions.

Answer

Place each component in a different subscription.

Question 6

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

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Azure Cache for Redis

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Azure SQL Database

Question 7

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

Accepted Answer

Evaluation. The Evaluation feature in Azure AI Foundry is specifically designed to assess model performance using metrics like accuracy, F1 score, and coherence. It provides built-in evaluators and supports custom evaluation flows, enabling systematic testing of models before and after deployment. This directly addresses the need to evaluate model performance, unlike other options which focus on monitoring, cataloging, or logging.

Answer

Endpoint monitoring

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Model Catalog

Answer

Deployment logs

Question 8

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 diagnostic logs is the correct choice because it captures granular, per-request telemetry from Azure OpenAI and Azure AI model deployments, including latency, token usage (prompt/completion tokens), HTTP status codes, and model-level metrics. Diagnostic settings route these logs to Log Analytics, where KQL queries can slice latency and token consumption by deployment, model, and time window in near real-time. This is the only option that exposes token-level metrics rather than aggregate cost or activity metadata.

Answer

Azure Billing dashboard

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Azure Activity Log

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Azure AI Foundry 'Overview' tab

Question 9

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.

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To perform data visualization.

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To manage firewall rules.

Question 10

You are configuring a connection to a vector store. What is the best way to handle the secret credential?

Accepted Answer

Store in Azure Key Vault and reference in the connection.. Key Vault is the centralized store for all application secrets in Azure, which should be linked to the project connection.

Answer

Hardcode the key in the flow configuration.

Answer

Pass it as a parameter in the code.

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Store it in an environment variable.

Question 11

You need to update a prompt flow without downtime. What deployment strategy should you use?

Accepted Answer

Use a Blue-Green deployment pattern.. Blue-Green deployment is the correct strategy because it involves running two identical environments (blue and green) where the new version is deployed to the inactive environment, tested, and then traffic is switched over. This allows updates without downtime and provides a quick rollback if issues arise. It is a standard pattern for zero-downtime deployments in cloud-native applications.

Answer

Directly overwrite the existing deployment.

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Stop the service during the update.

Answer

Delete the old deployment and create a new one.

Question 12

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

Accepted Answer

API Keys.. Option A (API Keys) is correct because Azure AI Foundry projects expose key-based authentication, where a project or resource key is passed in the request header (e.g., the api-key header) to authorize calls to the project's endpoints. Option B (Microsoft Entra ID with RBAC) is correct because Foundry projects support Entra ID token-based authentication, using role-based access control assignments (such as Cognitive Services User or Contributor) to authorize identities via OAuth 2.0 bearer tokens. Option C is not valid because hardcoding usernames in code is not an authentication mechanism and would be a security anti-pattern, not a supported credential type. Option D is not valid because local file-based passwords are not an authentication method offered by Azure AI Foundry. Option E is not valid because IP allow-listing is a network-level control, not an authentication method, and it does not verify caller identity.

Answer

Hardcoded usernames in the code.

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Local file-based passwords.

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Public IP allow-listing only.

Question 13

Which THREE of the following are components of an Azure AI Foundry project?

Accepted Answer

Prompt Flows.. In an Azure AI Foundry project, Prompt Flows (C) are a core component that lets you orchestrate prompts, tools, and logic into deployable AI workflows, so they are correctly listed. Connections (D) are also a project component because they define authenticated links from the project to external resources such as Azure OpenAI, Azure AI Search, or storage, enabling the project to consume those services. Models (E) are a component as well, since a Foundry project manages model deployments and references to models used by prompt flows and other assets. Azure Key Vault (A) is not a component of the project itself; it is a separate Azure resource that a project may connect to for secret management. Azure Subscriptions (B) are not a project component either, as a subscription is the billing and management boundary that contains the Foundry resource and project, not an internal part of the project.

Answer

Azure Key Vault (the resource itself).

Answer

Azure Subscriptions.

Question 14

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 15

You are deploying a model that requires specific GPUs. How do you ensure you get the right infrastructure?

Accepted Answer

Choose the correct SKU during model deployment.. Selecting the appropriate SKU (e.g., standard vs. GPU-optimized) during the deployment process ensures you get the required infrastructure.

Answer

Add the model to a resource group.

Answer

Update the OS of the node.

Answer

Request a quota increase for CPU.