AI-200 Practice Test 8 — 10 Questions

Question 1

An AI application hosted in Azure App Service throws intermittent socket exhaustion errors under heavy load when calling Azure OpenAI. You suspect improper HTTP client management. How should you fix this in .NET?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Use IHttpClientFactory to manage client lifecycles instead of instantiating new HttpClient objects per request.. In .NET, creating a new HttpClient per request leads to socket exhaustion because each instance holds its own connection pool and disposed clients leave sockets in TIME_WAIT. IHttpClientFactory pools and reuses HttpMessageHandler instances, resolving the exhaustion while still allowing per-request client configuration.

Answer

Configure a private endpoint between the App Service and Azure OpenAI.

Answer

Enable Always On in the App Service configuration settings.

Answer

Increase the TCP keep-alive timeout on the Azure App Service instance configuration.

Question 2

You are designing a RAG solution where documents are dynamically updated in Azure AI Search. You notice that search results return outdated information immediately after document updates. What is the cause of this behavior?

Accepted Answer

The indexer has not yet run its scheduled synchronization cycle to process changes from the data source. In Azure AI Search, data source changes are not automatically reflected in the index; an indexer must run to pull, transform, and load updated content. If the indexer is on a schedule (e.g., every 5 minutes or hourly), there is an inherent delay between the source update and the index refresh. Until that synchronization cycle completes, queries hit the old index data, returning outdated results. This is expected behavior, not a bug, and can be mitigated by triggering the indexer on-demand or using a change detection policy.

Answer

Semantic ranker caches search results for exactly 30 days

Answer

Azure OpenAI embedding models require a 24-hour cool-down period before new embeddings become valid

Answer

Vector indexes in Azure AI Search are read-only after creation and cannot accept updates

Question 3

You want to find out which Azure subscriptions and resource groups are incurring the highest costs for Azure Machine Learning compute instances this month. Where should you view this cost breakdown?

Accepted Answer

Cost Analysis grouped by Resource Group and filtered by Service Name 'Machine Learning'. Cost Analysis in Azure Cost Management allows you to group and filter costs by subscription, resource group, and resource type.

Answer

Azure Advisor security dashboard

Answer

Application Insights availability tests

Answer

Azure Monitor Metric alerts

Question 4

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 5

Your development team is planning to deploy a containerized natural language processing model to Azure Container Apps. Which TWO features are natively supported by Azure Container Apps for managing application lifecycle and traffic? (Choose two.)

Accepted Answer

Built-in KEDA-based autoscaling including scaling to zero. Azure Container Apps natively supports KEDA-based autoscaling, including scale-to-zero, which is option B — this is a core platform capability that lets containerized workloads (such as an NLP model) scale based on event sources like HTTP traffic or queues without managing Kubernetes infrastructure. Option C is also correct because Azure Container Apps provides native revision management, allowing immutable revisions of an app to be created and traffic splitting (for example, weighted percentages) between revisions for blue-green or canary deployments. Option A is incorrect because Azure Container Apps is a serverless container platform and does not deploy or expose bare-metal hypervisors to users. Option D is incorrect because Azure Container Apps does not provide direct root SSH access to individual container instances; debugging is done via logs, console, or exec into containers without root host access. Option E is incorrect because the Kubernetes control plane is fully managed by Azure and users cannot manually configure master nodes.

Answer

Automatic deployment of bare-metal hypervisors

Answer

Direct root SSH access to individual container instances for interactive debugging

Answer

Direct manual configuration of control plane Kubernetes master nodes

Question 6

Your enterprise application uses Azure Event Grid. Which TWO event schemas are natively supported when publishing events to an Event Grid custom topic? (Choose two)

Accepted Answer

CloudEvents 1.0 schema. Azure Event Grid custom topics natively support two event schemas for publishing: the CloudEvents 1.0 schema (option D) and the proprietary Azure Event Grid schema (option E). CloudEvents 1.0 is the CNCF-standardized, interoperable envelope format that Event Grid accepts directly, while the Azure Event Grid schema is the service's original native format with fields like 'eventType', 'subject', 'data', and 'dataVersion'. Both are selectable via the topic's input schema setting and are the only two schemas Event Grid accepts for custom topic publishing. Options A, B, and C are incorrect: Apache Kafka record batch binary, AMQP 1.0 binary stream envelopes, and SOAP 1.2 XML are not supported input schemas for Event Grid custom topics, even though Event Grid can deliver to Kafka or use AMQP internally for its own transport.

Answer

Apache Kafka record batch binary schema

Answer

AMQP 1.0 binary stream envelope

Answer

SOAP 1.2 XML messaging schema

Question 7

You need to deploy a set of batch AI processing containers in Azure Container Instances that run to completion and then terminate. Which container group restart policy should you select?

Accepted Answer

Never. For Azure Container Instances running batch workloads that execute to completion and then terminate, the restart policy must be Never. This tells ACI not to restart the container after it exits, allowing the container group to reach a terminal Succeeded state. It is the correct choice for run-once batch AI processing jobs.

Answer

Always

Answer

UnlessStopped

Answer

OnFailure

Question 8

You are designing an IoT data ingestion architecture using Azure Event Hubs. Devices occasionally send malformed JSON payloads that cause stream processing jobs to crash. You need to route these invalid messages to a separate error container without stopping the main ingestion stream. What should you implement?

Accepted Answer

Use Azure Stream Analytics with an error output data stream configured for malformed events.. Azure Stream Analytics supports an error output data stream that captures malformed or deserialization-failed events, allowing the main query to continue processing valid events while routing bad records to a separate sink (e.g., Blob Storage container). This is the documented pattern for handling malformed JSON without stopping the stream. Configuring the error output to point at an 'error' container satisfies the requirement.

Answer

Enable Event Hubs Capture with the JSON validation flag set to strict.

Answer

Set up an Event Grid system topic filter for 'MalformedEvent'.

Answer

Configure the Service Bus dead-letter queue on the Event Hub producer client.

Question 9

You need to view the logs of a container that recently crashed in an AKS cluster. Which command is used?

Accepted Answer

kubectl logs --previous.. The `kubectl logs --previous` command retrieves the logs from the previous (crashed) container instance in a pod. When a container crashes and restarts, the current container's logs may be empty or show only the new startup, so the `--previous` flag is essential to access the terminated container's output. This is the standard kubectl mechanism for post-mortem debugging of crashed containers in AKS.

Answer

kubectl get pods.

Answer

az aks logs.

Answer

kubectl describe pod.

Question 10

You need to ensure that traffic between your Azure App Service hosting an AI wrapper and your Azure AI Search instance does not traverse the public internet. Which feature should you implement?

Accepted Answer

Configure a Private Endpoint for the Azure AI Search service and integrate your App Service with the virtual network.. Azure Private Endpoint provides private IP addresses from your VNet to securely connect to Azure AI Search, keeping traffic off the public internet.

Answer

Enable Cross-Origin Resource Sharing (CORS) with a restricted origin list.

Answer

Enable TLS mutual authentication (mTLS) on the Azure AI Search service.

Answer

Configure IP access control lists on the Azure AI Search firewall to allow only the App Service outbound IPs.