AI-200 Practice Test 23 — 15 Questions

Question 1

To optimize costs and monitor consumption for an Azure OpenAI deployment across multiple business units, which THREE practices should you implement? (Choose three)

Accepted Answer

Apply Azure resource tags representing cost centers or business units to all AI service instances.. Option A is correct because applying Azure resource tags that represent cost centers or business units to every AI service instance enables cost allocation and chargeback reporting in Azure Cost Management, which is essential for tracking consumption per business unit. Option C is correct because Azure Monitor exposes token usage metrics for Azure OpenAI, and analyzing those patterns lets you decide whether Provisioned Throughput Units (PTUs) or Pay-As-You-Go token billing is cheaper for each workload's utilization profile. Option E is correct because Azure Budgets support alert actions (email, action groups, automation) that notify department heads when actual or forecasted spend reaches a threshold such as 80% of allocation, enabling proactive cost control. Option B is wrong because deleting Log Analytics diagnostic logs immediately destroys the telemetry needed for monitoring and auditing, and diagnostic logs are not the primary driver of Azure OpenAI cost. Option D is wrong because permanently provisioning maximum PTUs regardless of active workload wastes money, since PTUs are billed hourly whether or not they are used.

Answer

Delete all Log Analytics diagnostic logs immediately upon generation to reduce storage billing.

Answer

Provision maximum possible provisioned throughput units permanently regardless of active workload.

Question 2

Your enterprise system uses Azure Service Bus. Which TWO mechanisms can you implement to handle 'poison' or repeatedly failing messages? (Choose two)

Accepted Answer

Configuring Max Delivery Count and automatic dead-lettering. Option D is correct because Service Bus queues and subscriptions have a MaxDeliveryCount property (default 10); once a message's delivery count exceeds this value, the broker automatically moves it to the dead-letter queue ($DeadLetterQueue), which is the built-in mechanism for isolating poison messages. Option E is correct because the application can proactively dead-letter a message by calling DeadLetterMessageAsync() (or DeadLetterAsync() on the ServiceBusReceiver/MessageReceiver) when it detects a non-retryable or repeatedly failing message, giving explicit control over poison-message handling. Option A is incorrect because Auto-Inflate only scales messaging units/throughput capacity and has nothing to do with message failure handling. Option B is incorrect because Event Grid advanced filters apply to event subscriptions for Event Grid events, not to Service Bus queue message processing or dead-lettering. Option C is incorrect because setting a null Partition Key only affects session/partition routing behavior and does not address poison messages.

Answer

Configuring Auto-Inflate on the Service Bus namespace

Answer

Enabling Event Grid advanced filters on the queue

Answer

Setting the Partition Key to a null value

Question 3

You need to deploy a quick, stateless AI data processing script packaged in a container image without setting up Kubernetes or Container Apps. Which Azure service offers the fastest deployment for a single container instance?

Accepted Answer

Azure Container Instances. Azure Container Instances (ACI) is a serverless container runtime that deploys a single container (or container group) in seconds via a single CLI command or ARM template, with no cluster or orchestrator to provision. It bills per-second for vCPU and memory consumed, making it ideal for short-lived, stateless AI data processing jobs. Because there is no node pool, ingress controller, or control plane to manage, ACI delivers the fastest path from image to running container.

Answer

Azure Virtual Machines with Docker installed

Answer

Azure App Service for Linux

Answer

Azure Kubernetes Service

Question 4

An enterprise application uses Azure AI Search and needs to authenticate securely to an Azure OpenAI backend without exposing credentials in connection strings. What is the recommended approach?

Accepted Answer

Configure a system-assigned managed identity on the Azure AI Search service and assign the Cognitive Services OpenAI User role on the Azure OpenAI resource.. A system-assigned or user-assigned managed identity should be enabled on the Azure AI Search service and granted the Cognitive Services OpenAI User role on the Azure OpenAI resource.

Answer

Create a shared access signature (SAS) token for the Azure OpenAI endpoint and rotate it every 30 days.

Answer

Use HTTP basic authentication with a pre-shared service principal secret.

Answer

Store the Azure OpenAI primary key in an Azure App Configuration service and reference it via Key Vault.

Question 5

You are writing a Python worker application that consumes messages from an Azure Service Bus queue. To prevent message loss during unexpected application crashes, you need to ensure that messages are locked while being processed and only deleted from the queue after successful processing. Which receive mode should you use?

Accepted Answer

PeekLock mode. PeekLock mode is correct because it locks the message for a configurable duration while the consumer processes it, and the message is only deleted after the consumer explicitly completes it. If the application crashes, the lock expires and the message becomes available again, preventing message loss. ReceiveAndDelete would remove the message immediately upon receipt, risking loss on failure.

Answer

Deferred mode

Answer

SessionLock mode

Answer

ReceiveAndDelete mode

Question 6

You are writing a Python script to query an Azure AI Search index using the azure-search-documents SDK. You want to retrieve both vector matches and keyword matches using hybrid search. Which class should you instantiate to represent the vector query?

Accepted Answer

VectorizedQuery. In the azure-search-documents Python SDK, a vector query is represented by the VectorizedQuery class, which holds the embedding vector, the k-nearest-neighbors count, and the fields to search. You pass a list of VectorizedQuery objects to the search client's search method alongside a keyword query string to perform hybrid search. This class is the SDK's canonical representation of a vector query payload.

Answer

EmbeddingQueryClient

Answer

HnswSearchRequest

Answer

CosmosVectorSearch

Question 7

You are designing an enterprise messaging solution using Azure Service Bus. You need to ensure that messages published within a transaction (involving multiple messages sent to different queues) either all succeed or all fail together. What feature should you use?

Accepted Answer

Service Bus client transaction scope (TransactionScope in .NET). The correct feature is the Service Bus client transaction scope (TransactionScope in .NET). Azure Service Bus supports transactions that allow multiple messages sent to different queues or topics to be committed atomically. Using TransactionScope in .NET, developers can group send operations so that either all messages are committed or none are. This ensures consistency across multiple entities within a single transaction.

Answer

Storage Account atomic blob leases

Answer

Event Grid atomic batch publishing mode

Answer

Event Hubs transaction coordinator checkpointing

Question 8

Which distance metric in Azure Cosmos DB vector search calculates the inner product of two vectors, measuring both magnitude and angle?

Accepted Answer

dotproduct. The dotproduct distance metric in Cosmos DB vector search computes the inner product of two vectors, which incorporates both magnitude and angle. It is commonly used when vector magnitudes carry meaning, such as in recommendation systems where popularity or confidence scores matter.

Answer

cosine

Answer

euclidean

Answer

manhattan

Question 9

You need to review the security recommendations provided by Microsoft for your Azure AI resources, such as enabling HTTPS-only traffic and configuring managed identities. Which Azure tool provides these prioritized security recommendations and a security score?

Accepted Answer

Microsoft Defender for Cloud. Microsoft Defender for Cloud continuously assesses Azure resources — including Azure AI services — against security benchmarks and produces prioritized recommendations plus a secure score. Recommendations such as enabling HTTPS-only traffic, configuring managed identities, and restricting network access appear directly in Defender for Cloud's regulatory compliance and recommendations blades. This is the tool Microsoft positions for cloud security posture management (CSPM).

Answer

Azure Cost Management

Answer

Azure Monitor Metrics

Answer

Azure Service Health

Question 10

You are managing an Azure Service Bus Premium namespace. Your application requires strict transactional messaging where a worker receives a message from Queue A, processes it, and sends a resulting message to Queue B. If any step fails, both actions must be rolled back. How should you implement this?

Accepted Answer

Wrap the receive and send operations inside a .NET TransactionScope block.. In Azure Service Bus, transactional messaging across multiple entities (like receiving from one queue and sending to another) can be achieved using the .NET TransactionScope class. This ensures that both operations are committed or rolled back together.

Answer

Set the message session ID to the same value on both queues.

Answer

Enable Event Grid automatic forwarding between queues.

Answer

Use Event Hubs checkpoints for transactional rollback.

Question 11

You are troubleshooting a scenario where an Azure App Service hosting an AI frontend cannot communicate with an Azure OpenAI endpoint secured by a private endpoint in a different virtual network. Which THREE of the following networking configurations should you verify? (Choose three)

Accepted Answer

Virtual network peering between the App Service VNet and the private endpoint VNet.. Option B is correct because the App Service VNet and the private endpoint VNet must be peered (or otherwise connected via a gateway/transit) so that traffic can flow between them; without peering, the App Service cannot reach the private endpoint's private IP. Option C is correct because App Service must have regional VNet integration enabled, which routes outbound application traffic from the app into the delegated integration subnet, allowing it to reach the peered VNet and the private endpoint. Option E is correct because the private endpoint's FQDN (e.g., <resource>.privatelink.openai.azure.com) must resolve to the private IP via a private DNS zone linked to the relevant VNets; without this DNS integration, name resolution returns the public IP and the connection fails. Option A is not relevant because Azure Front Door geo-filtering affects inbound HTTP traffic distribution, not internal DNS resolution or App Service-to-private-endpoint connectivity. Option D is not relevant because local admin passwords on worker instances have no bearing on VNet routing, peering, or private DNS resolution for private endpoint access.

Answer

Azure Front Door geo-filtering rules blocking all international DNS queries.

Answer

Local admin passwords on the App Service worker instances.

Question 12

Your team is implementing hybrid search in Azure AI Search combining full-text keyword search and vector search. Users report that certain exact product serial numbers are missing from the semantic ranking results. Which query parameter should you adjust to balance the influence of BM25 text search scores against vector cosine similarity scores?

Accepted Answer

Enabling semantic ranking and providing a semanticConfiguration with targeted weight overrides.. When performing hybrid queries in Azure AI Search, the search engine uses Reciprocal Rank Fusion (RRF) to combine results from text and vector searches. You can influence the weighting or use standard RRF parameters, but adjusting semantic ranking configurations or ensuring proper semantic configuration boosts can address relevance blending. Specifically, adjusting query parameters like semantic configuration or custom scoring profiles allows fine-tuning.

Answer

Increasing the efSearch parameter in the index definition.

Answer

Adjusting the top parameter to retrieve a larger candidate pool before RRF combination.

Answer

Switching the vector similarity metric from cosine to euclidean distance.

Question 13

You have an AI workload that requires persistent storage for large model checkpoints. Which storage type should you mount to an AKS pod for optimal performance?

Accepted Answer

Azure Disk (Premium).. Azure Disk (Premium) is the correct choice because it provides low-latency, high-IOPS block storage that can be mounted as a ReadWriteOnce volume to an AKS pod via a PersistentVolumeClaim. For large model checkpoints that require fast sequential and random I/O during training or inference, Premium SSD-backed Azure Disks deliver the performance Azure Files Standard cannot match.

Answer

Azure Files (Standard).

Answer

Ephemeral OS Disk.

Answer

Azure Blob Storage (container mount).

Question 14

You are configuring continuous deployment for an AI model container. Whenever a data scientist pushes code to a GitHub repository, you want Azure Container Registry to automatically build and tag the new container image without managing an external CI/CD build agent. What should you use?

Accepted Answer

ACR Tasks with automated source code triggers. ACR Tasks supports automated builds triggered by source code commits to GitHub or Azure DevOps, executing the docker build inside Azure without requiring you to manage a build agent. You configure a task with a source trigger (e.g., on commit to a branch) and ACR builds, tags, and optionally pushes the image automatically. This matches the requirement for CI/CD without external build infrastructure.

Answer

Azure DevOps self-hosted build agent running on a VM

Answer

Azure Logic Apps polling GitHub webhooks

Answer

An AKS deployment controller pod

Question 15

Your enterprise application uses Azure Service Bus Topics to broadcast AI model inference updates to multiple microservices. One particular subscriber requires filtering messages so it only receives updates where the 'ModelVersion' property is equal to 'v2' and the 'ConfidenceScore' is greater than 0.85. How should you implement this?

Accepted Answer

Create a SQL filter on the Service Bus subscription with the expression ModelVersion = 'v2' AND ConfidenceScore > 0.85.. Azure Service Bus subscriptions support SQL filters that evaluate against both system properties and user-defined custom properties (also called application properties) on the message. Because 'ModelVersion' and 'ConfidenceScore' are custom properties set by the publisher, a SQL filter expression like ModelVersion = 'v2' AND ConfidenceScore > 0.85 evaluates server-side at the subscription, so only matching messages are delivered to that subscriber. This is the native, most efficient mechanism for content-based routing on Service Bus Topics.

Answer

Create a boolean filter on the subscription matching a correlation ID of 'v2'.

Answer

Write custom middleware in the subscriber service to drop messages that do not meet the criteria after reading them from the topic.

Answer

Configure an Event Grid custom filter using advanced filters with an 'And' operator.