AI-300 Genaiops Infrastructure Practice Test 2 — 15 Questions

Question 1

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.. Resource Tags on the Azure AI project resource allow you to categorize and track costs by project. By applying tags (e.g., Project: A), you can use Azure Cost Management to filter and analyze costs per tag, enabling per-project cost tracking. This is the recommended approach for cost allocation in Azure.

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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 2

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.. Using an Azure API Management (APIM) policy to implement a retry pattern is the correct approach for infrastructure-level resiliency. APIM acts as a gateway that can intercept requests to backend LLM services and apply policies such as retry, circuit breaker, and rate limiting. This offloads resiliency logic from the application code and provides a centralized, configurable way to handle transient errors across multiple LLM services.

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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 3

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 4

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.. Storing the key in Azure Key Vault and referencing it as a Connection in the AI Project is correct because Key Vault provides secure secret management with access policies, auditing, and encryption. The AI Project can securely retrieve the key at runtime without exposing it in code or configuration files. Other options expose the key insecurely.

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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 5

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 6

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 asynchronous execution patterns for long-running tools is correct because it allows the agent orchestration to continue processing other tasks or handle timeouts gracefully without blocking on a single tool call. This decouples the tool's execution time from the agent's overall timeout, improving responsiveness and scalability. Increasing CPU or global timeout does not address the fundamental issue of synchronous blocking.

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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 7

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

Accepted Answer

Azure AI Foundry -> Project -> Deployments. Model deployment settings — including the deployed model version, instance count (capacity), deployment type, and rate limits — are configured in Azure AI Foundry under the project's Deployments blade. This is where you create and manage deployments that expose a model endpoint for inference, and where you scale capacity and pin versions. It is the operational control plane for serving models.

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Azure Portal -> Subscriptions

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Azure Key Vault -> Secrets

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Azure Monitor -> Alerts

Question 8

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 9

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. Prompt versioning in the AI Foundry workflow is managed through Prompt Flow, which treats prompts as versioned flow assets. Prompt Flow lets you author, version, and compare prompt variants, track them alongside flow runs, and promote a specific version to a deployment, giving reproducibility and rollback. This is the native mechanism for prompt lifecycle management in Azure AI Foundry.

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Azure Database for PostgreSQL

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GitHub repository only

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Azure Blob Storage

Question 10

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.

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The 'Keys' tab in the Azure Portal

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Azure Subscription usage reports

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Azure Storage Account logs

Question 11

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.

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Create separate Azure subscriptions for every environment.

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Hardcode values in the application code.

Question 12

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 in Azure AI Foundry provides a curated collection of pre-optimized models from various providers, including OpenAI, Meta, and Hugging Face, that can be deployed with one-click deployment. This simplifies the process of selecting, deploying, and managing models for inference, saving time and reducing configuration errors. The models are pre-validated and optimized for Azure infrastructure, ensuring performance and compatibility.

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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 13

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.. The generation phase is handled by the LLM, not the vector database or embedding model. Increasing the 'Instances' count for the LLM deployment provides more compute capacity to handle concurrent generation requests, directly reducing latency. This is the correct scaling action because the bottleneck is identified as the generation phase, which is compute-bound on the LLM inference servers.

Answer

Optimize the vector database index.

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Reduce the 'temperature' setting.

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Increase the 'Embedding Model' throughput.

Question 14

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. Azure AI Services resources (including AI Foundry projects backed by Azure AI Services) expose a Networking (Firewall) configuration blade where you can set 'Selected Networks' and specify allowed IP addresses or CIDR ranges, or disable public access entirely. This is the native, resource-level control for restricting inbound traffic to specific IPs.

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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 15

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' tab in Azure AI Foundry provides a real-time view of your current token usage against the allocated quota for Azure OpenAI models. This is the quickest way to check if you have hit your token quota because it directly shows usage and limits without needing to wait for billing cycles or parse logs. It aggregates usage across deployments and models, allowing immediate action.

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Review your monthly invoice.

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Check the Azure Monitor 'Errors' log.

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Check the model endpoint deployment logs.