AI-200 Containerized AI Workloads Practice Test 6 — 15 Questions

Question 1

You are configuring an Azure Kubernetes Service (AKS) cluster for enterprise AI workloads. Which THREE practices should you implement to enhance cluster security and isolation? (Choose three.)

Accepted Answer

Configure Microsoft Entra Workload Identity for pod-to-Azure service authentication. Option A is correct because Microsoft Entra Workload Identity federates Kubernetes service accounts with Microsoft Entra ID via OIDC, letting pods authenticate to Azure services (Key Vault, Storage, etc.) without storing secrets or using a shared service principal. Option D is correct because Azure Policy for Kubernetes (via the Gatekeeper admission controller) enforces governance and compliance at the cluster level, such as restricting privileged containers, hostPath mounts, and allowed registries. Option E is correct because integrating Microsoft Entra ID with Azure RBAC for Kubernetes authorization enables centralized, identity-based access control to the API server, replacing static local accounts and supporting least-privilege role assignments. Option B is incorrect because disabling TLS on the API server exposes credentials and traffic to interception and violates security best practices; performance gains do not justify it. Option C is incorrect because sharing a single service principal across all namespaces eliminates isolation, creates a single credential to compromise, and prevents per-workload least-privilege scoping.

Answer

Disable TLS on the API server to improve inter-node communication speed

Answer

Share a single service principal across all namespaces for simplified administration

Question 2

Your organization requires that container images stored in Azure Container Registry (ACR) comply with security standards. Which TWO actions can you take to secure container images in ACR? (Choose two.)

Accepted Answer

Enable Microsoft Defender for Cloud vulnerability scanning for ACR registries. Option D is correct because enabling Microsoft Defender for Cloud's vulnerability scanning for ACR registries continuously scans images for known CVEs and provides remediation guidance, directly supporting image security compliance. Option E is correct because enabling content trust (Docker Content Trust) enforces image signing and verification, ensuring only trusted, signed images are pulled or deployed, which prevents tampered or unauthorized images from being used. Option A is wrong because granting Contributor permissions to every external guest user violates least-privilege and expands the attack surface rather than securing images. Option B is wrong because storing registry administrator credentials in public GitHub repositories exposes secrets and enables unauthorized access. Option C is wrong because disabling network firewalls and allowing unrestricted public pulling removes network-level protections and increases exposure of the registry.

Answer

Grant Contributor permissions to every external guest user

Answer

Store registry administrator credentials in public GitHub repositories for easy team access

Answer

Disable all network firewalls on the registry to allow unrestricted public pulling

Question 3

When managing Azure Container Registry (ACR), administrators can use ACR Tasks to automate tasks. Which TWO scenarios are valid use cases for ACR Tasks? (Choose two.)

Accepted Answer

Triggering container image rebuilds automatically when a base image is updated. Option A is correct because ACR Tasks supports base image update triggers, which automatically rebuild dependent images when the base image in the same or another registry is updated, keeping images patched and current. Option E is correct because ACR Tasks provides source code triggers, including GitHub commit and pull request triggers, that automatically build and push container images when code changes are committed. Option B is incorrect because a managed Kubernetes API control plane is provided by Azure Kubernetes Service (AKS), not ACR. Option C is incorrect because ACR Tasks builds and runs container workloads; vulnerability scanning is provided by Microsoft Defender for Containers or third-party integrations, not by executing scans inside registry storage. Option D is incorrect because ACR is a container image and artifact registry, not a platform for running persistent multi-node database clusters.

Answer

Providing a managed Kubernetes API control plane

Answer

Executing automated container vulnerability scans directly inside registry storage

Answer

Running a persistent, multi-node enterprise database cluster inside ACR

Question 4

You are configuring secure ingress and network isolation for an Azure Container Apps environment. Which THREE capabilities are supported when configuring networking for Container Apps? (Choose three.)

Accepted Answer

Exposing the environment with either external (public) or internal (private VNet) ingress. Option A is correct because Azure Container Apps environments support both external ingress, which exposes apps to the public internet via a public endpoint, and internal ingress, which restricts access to a private VNet or the environment's internal network. Option C is correct because you can deploy a Container Apps environment into a customer-managed Azure Virtual Network subnet, enabling network isolation, private endpoints, and control over inbound/outbound traffic. Option E is correct because Container Apps supports custom domain names with automated managed TLS/SSL certificates, including free managed certificates for custom domains and binding of existing certificates. Option B is incorrect because customers cannot directly modify physical Azure datacenter top-of-rack switches; Azure manages the physical network infrastructure. Option D is incorrect because attaching physical Fibre Channel SAN arrays is not a supported Container Apps networking capability; persistent storage in Container Apps is provided through Azure Files or managed storage abstractions, not direct SAN attachment.

Answer

Direct modification of physical Azure datacenter top-of-rack switches

Answer

Direct attachment of physical Fibre Channel storage SAN arrays

Question 5

Your team is evaluating Azure Container Apps versus Azure Kubernetes Service (AKS) for hosting containerized AI workloads. Which TWO characteristics are advantages of Azure Container Apps over standard AKS? (Choose two.)

Accepted Answer

Simplified container management without direct exposure or management of Kubernetes master nodes. Option B is correct because Azure Container Apps is a serverless, fully managed container platform built on Kubernetes and KEDA, so the underlying Kubernetes control plane (master nodes) is abstracted away and never exposed to the customer for patching or management, unlike AKS where you are responsible for node pools and cluster operations. Option D is correct because Container Apps natively supports scale-to-zero via its built-in KEDA-based scaling rules, meaning idle workloads consume no compute and incur no cost, whereas standard AKS requires configuring cluster autoscaler or KEDA manually and typically keeps at least one node running. Option A is incorrect because Container Apps does not require you to manage CNI overlay routing tables; that is a low-level networking task associated with self-managed Kubernetes or advanced AKS CNI configurations. Option C is incorrect because KEDA and ingress are built into Container Apps and configured declaratively, not mandatory manual controller installations. Option E is incorrect because Container Apps deliberately restricts low-level administrative access, including kernel modules and root certificates, which is the opposite of an advantage over AKS.

Answer

Requirement to manage custom CNI overlay networking routing tables manually

Answer

Mandatory manual configuration of KEDA controllers and Ingress controllers

Answer

Complete low-level administrative access to kernel modules and cluster root certificates

Question 6

You are managing an Azure Kubernetes Service (AKS) cluster that runs high-throughput AI model training jobs using NVIDIA Tensor Core GPUs. Pods frequently fail to schedule because of insufficient GPU memory across existing nodes. You need to ensure the cluster automatically provisions new GPU-enabled worker nodes when resource demands increase. What should you configure?

Accepted Answer

Enable the Kubernetes Cluster Autoscaler on a node pool containing GPU virtual machine sizes.. The Kubernetes Cluster Autoscaler watches for pods that cannot be scheduled due to insufficient resources and automatically scales the node pool up or down. When configured on a node pool that contains GPU-enabled VM sizes (such as NC or ND series), it will provision additional GPU nodes when training pods are pending due to GPU memory constraints. This is the only option that adds new worker nodes rather than scaling existing pods or reacting to events.

Answer

Configure KEDA (Kubernetes Event-driven Autoscaling) with a Prometheus scaler.

Answer

Configure the Horizontal Pod Autoscaler (HPA) with custom GPU metrics.

Answer

Deploy the Azure Container Storage operator with GPU caching enabled.

Question 7

You are containerizing a large language model (LLM) inference API to run in Azure Container Apps. The model weights exceed 15 GB and cause container startup timeouts because the image takes too long to pull from Azure Container Registry (ACR). You need to optimize the container startup performance without reducing the model size. What is the best approach?

Accepted Answer

Enable ACR artifact streaming for the container registry.. ACR artifact streaming lets ACR stream image layers directly to the container runtime on demand rather than requiring the full image to be pulled and extracted before startup. This dramatically reduces cold-start time for large images like LLM weight layers, without shrinking the model. It is the purpose-built ACR feature for exactly this startup-latency problem.

Answer

Upgrade the Azure Container Apps environment to use HTTP/2 protocol.

Answer

Store the model weights in an Azure Blob Storage account and mount them via an ephemeral volume.

Answer

Configure ACR content trust and geo-replication.

Question 8

You are deploying an AI model container to Azure Container Apps. The container application needs to securely connect to an Azure SQL Database without storing plain-text database credentials in environment variables or application code. What is the recommended feature to use?

Accepted Answer

Azure Key Vault references with managed identity. Azure Key Vault references combined with a managed identity let the container retrieve secrets at runtime without embedding credentials in code or environment variables. The managed identity authenticates to Key Vault, and the Key Vault reference resolves the secret into the app's configuration securely.

Answer

Azure Container Registry admin credentials

Answer

Container Apps encrypted environment variables

Answer

Kubernetes secrets mapped via sidecar proxy

Question 9

You deploy an AI inferencing service to an Azure Kubernetes Service (AKS) cluster. The service uses an ONNX Runtime container. Users report intermittent 504 Gateway Timeouts during peak batch request loads. You notice that the application pods are being terminated due to Out-Of-Memory (OOM) errors during heavy inference loads. How should you resolve this issue in your Kubernetes deployment manifest?

Accepted Answer

Increase the memory limit value in the container resource specifications.. The pods are being OOM-killed because the container's memory limit is lower than the peak memory required during heavy inference. Increasing the memory limit in the container resource specifications gives the ONNX Runtime process enough headroom to complete batch inference without being terminated by the kernel OOM killer. This directly addresses the root cause of the 504 timeouts, which occur because pods die mid-request.

Answer

Decrease the readiness probe failureThreshold to remove faulty pods faster.

Answer

Enable the Kubernetes Horizontal Pod Autoscaler based on CPU utilization.

Answer

Configure a liveness probe with an increased initialDelaySeconds value.

Question 10

You are configuring continuous deployment for an AI model container using Azure DevOps and Azure Container Registry (ACR). You need to ensure that when a new container image is pushed to ACR, an event triggers an automated webhook that notifies an Azure Function to run post-deployment validation tests. Which event type should you subscribe to in the ACR webhook configuration?

Accepted Answer

imagePush. The imagePush event fires whenever a new container image or tag is pushed to the registry, which is exactly the trigger needed to invoke a webhook that notifies an Azure Function for post-deployment validation. ACR webhooks subscribe to specific event types, and imagePush is the canonical event for CI/CD pipelines reacting to new images.

Answer

artifactDelete

Answer

chartPush

Answer

quarantineStatusChange

Question 11

You are designing an MLOps pipeline on Azure Kubernetes Service (AKS) for a computer vision model. You need to configure GPU sharing among multiple inference pods on the same node to optimize cost. Which TWO actions should you take? Each correct answer presents part of the solution.

Accepted Answer

Enable GPU time-slicing or NVIDIA Multi-Instance GPU (MIG) configuration.. Option B is correct because GPU sharing on AKS is achieved through NVIDIA's time-slicing or Multi-Instance GPU (MIG) configuration, which partitions a single physical GPU so multiple inference pods can run concurrently on the same node, directly reducing cost. Option E is correct because the NVIDIA GPU Operator automates the deployment and lifecycle of the GPU device plugin, drivers, container runtime, and related components on AKS, which is a prerequisite for enabling and managing time-slicing or MIG configurations. Option A is incorrect because a system-node-critical priority class only affects scheduling priority and eviction behavior; it does not enable GPU sharing. Option C is incorrect because KEDA with an Azure Queue trigger provides event-driven autoscaling and has nothing to do with partitioning or sharing a GPU among pods. Option D is incorrect because Azure CNI dynamic IP allocation addresses pod IP address management and networking, not GPU resource sharing.

Answer

Set the pod priority class to system-node-critical.

Answer

Deploy the KEDA scaler with an Azure Queue trigger.

Answer

Configure the Azure CNI plugin for dynamic IP allocation.

Question 12

You are troubleshooting a containerized AI model deployment in Azure Container Apps that fails to pass its HTTP readiness probe. You need to inspect the container logs and runtime diagnostics. Which THREE tools or commands can you use to troubleshoot this issue? Each correct answer presents a complete solution.

Accepted Answer

Access the Azure Portal and use the Console diagnostic tool for the specific container app revision.. Option A is correct because the Azure Portal's Console diagnostic tool for a specific container app revision opens an interactive shell inside the running container, letting you inspect the filesystem, processes, and probe-related runtime behavior directly. Option B is correct because 'az containerapp logs show' retrieves the system and console log streams for a container app (with parameters like --name, --resource-group, and --revision), which is exactly what you need to see why the HTTP readiness probe is failing. Option C is correct because Container Apps environments can send console and system logs to a Log Analytics workspace, where you can query them with KQL (for example, the ContainerAppConsoleLogs_CL and ContainerAppSystemLogs_CL tables) to diagnose probe failures. Option D is not correct because 'az aks browse' targets Azure Kubernetes Service clusters, not Azure Container Apps, and it opens a Kubernetes dashboard rather than exposing container logs. Option E is not correct because 'kubectl describe pod' works against a Kubernetes API server and pods, whereas Container Apps is a serverless platform that abstracts away pods and does not expose them to kubectl.

Answer

Execute az aks browse to open the Kubernetes dashboard.

Answer

Use kubectl describe pod to inspect Kubernetes events.

Question 13

You are optimizing an AI model container image size. Which technique is most effective for reducing the footprint of an image based on a large Python deep learning framework?

Accepted Answer

Implement multi-stage Docker builds.. Multi-stage Docker builds let you compile/install dependencies in a builder stage and copy only the runtime artifacts into a slim final stage, discarding build tools, caches, and intermediate files. For a large Python deep learning framework (e.g., PyTorch/TensorFlow), this can cut image size dramatically because compilers, pip caches, and dev headers never reach the final image. It is the standard technique for shrinking ML container images without losing functionality.

Answer

Enable ACR Georeplication.

Answer

Store model weights as image layers.

Answer

Use the 'latest' tag for all base images.

Question 14

You need to store private AI model weights in an Azure Container Registry (ACR). Which authentication method follows the principle of least privilege for an AKS cluster to pull these images?

Accepted Answer

Azure Managed Identity.. Azure Managed Identity lets the AKS cluster authenticate to ACR without storing any credentials, and the identity can be granted only the AcrPull role scoped to the specific registry. This satisfies least privilege because the cluster gets exactly the permission needed (pull images) and nothing more, with no secrets to rotate or leak. It is the recommended, Microsoft-supported approach for AKS-to-ACR authentication.

Answer

Service Principal with Contributor role.

Answer

ACR Admin account credentials.

Answer

A shared SAS token generated for the registry.

Question 15

An AKS cluster is configured with KEDA for autoscaling an AI inferencing service based on Azure Queue Storage length. The pods fail to scale. Which component should you inspect first?

Accepted Answer

The ScaledObject resource status.. KEDA works by creating and managing a Horizontal Pod Autoscaler behind the scenes, but the user-facing configuration object is the ScaledObject, which defines the trigger (Azure Queue Storage), min/max replicas, and target workload. If scaling fails, the ScaledObject's status conditions and events reveal authentication errors, trigger misconfiguration, or missing metadata. Inspecting it first is the fastest path to the root cause.

Answer

The Azure Container Registry webhook settings.

Answer

The Horizontal Pod Autoscaler (HPA) resource directly.

Answer

The Cluster Autoscaler logs.