AI-200 Practice Test 4 — 10 Questions

Question 1

You are troubleshooting a latency issue with an AI model deployed in a Virtual Machine. You suspect the issue is related to storage IOPS limits. Which tool should you use to check the storage performance metrics?

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

Accepted Answer

Azure Monitor Metrics for the Virtual Machine.. Azure Monitor 'Metrics' for the Virtual Machine resource provides 'Disk Read Operations/sec' and 'Disk Write Operations/sec', which are key for diagnosing IOPS bottlenecks.

Answer

Azure Network Watcher.

Answer

Azure Resource Graph Explorer.

Answer

Application Insights Dependency logs.

Question 2

You are implementing an event-driven architecture where an Azure Function acts as an Event Grid subscriber. Occasionally, the downstream cognitive service is throttled, causing failures. You need to ensure that unprocessable events are not lost and can be inspected later. Where should you configure a dead-letter destination?

Accepted Answer

On the Event Grid event subscription, specify an Azure Blob Storage container as the dead-letter endpoint.. Event Grid event subscriptions support a native dead-letter destination where events that cannot be delivered (after retries are exhausted) are stored as blobs in an Azure Storage container. This is configured on the event subscription itself, not on the subscriber, because Event Grid owns the delivery and retry lifecycle. The dead-lettered blob retains the original event payload plus failure metadata, enabling later inspection and reprocessing.

Answer

On the Azure Function trigger binding definition using the deadLetterQueueName property.

Answer

On the Azure Storage account firewall settings as an IP filter exception.

Answer

Inside the Azure Function application configuration settings as an Application Insights error sink.

Question 3

You need to monitor the monthly cost of an Azure AI Document Intelligence resource to ensure it does not exceed a specified budget. Where should you configure an automated notification when the cost reaches 80% of the budget?

Accepted Answer

Create a Budget in Azure Cost Management + Billing with an alert threshold set to 80%.. Azure Cost Management + Billing Budgets are the native mechanism for setting spending thresholds and triggering automated notifications (email or action groups) when actual or forecasted cost reaches a defined percentage of the budget. Setting an 80% threshold on a budget scoped to the Document Intelligence resource is the correct approach.

Answer

Configure an alert rule in Azure Monitor on the Document Intelligence resource metric 'Transactions'.

Answer

Set up an action group in Azure Advisor to monitor billing anomalies.

Answer

Create an expenditure alert inside the Azure AI Foundry portal.

Question 4

Which THREE logs or telemetry types should you collect in Azure Monitor to comprehensively troubleshoot an AI-powered web application?

Accepted Answer

Application requests and response status codes.. Application Insights collects request telemetry, dependency tracking for API calls, and trace logs for debugging logic.

Answer

Azure Bastion audit logs.

Answer

Azure Firewall packet capture PCAP files.

Question 5

Your enterprise AI application is deployed across multiple Azure regions for high availability. You need to ensure that logs from Application Insights instances in all regions are aggregated into a single central Log Analytics workspace for unified querying. How should you configure this?

Accepted Answer

Configure each Application Insights resource to use the same shared centralized Log Analytics workspace during creation. Application Insights resources can be configured to send their telemetry to a Log Analytics workspace, and pointing multiple regional Application Insights instances at the same workspace aggregates all logs for unified KQL querying. This is done at creation time (or via migration) by selecting the shared workspace as the ingestion target. The result is a single pane of glass across regions without custom ETL.

Answer

Deploy a separate Log Analytics workspace in every region and use Azure Backup to copy tables

Answer

Write a custom Python script that downloads logs from each region every hour and uploads them to Azure Blob Storage

Answer

Use Azure Traffic Manager to merge log data streams at the DNS level

Question 6

Your application publishes messages to an Azure Service Bus queue. The messages contain sensitive personal data. Enterprise compliance mandates that message payloads are encrypted end-to-end at the application layer before being sent to Service Bus. How should you implement this?

Accepted Answer

Encrypt the message payload in your application code using a key from Azure Key Vault before sending, and decrypt upon receipt.. End-to-end application-layer encryption means the payload must be encrypted before it leaves the producer and decrypted only by the consumer, so the broker never sees plaintext. Using a key from Azure Key Vault to encrypt in code and decrypt on receipt satisfies this because Service Bus only transports ciphertext, and key custody stays in Key Vault.

Answer

Configure Event Grid advanced filters to redact sensitive keys.

Answer

Enable Service Bus Premium Geo-Disaster Recovery with encryption enabled.

Answer

Use Shared Access Signature tokens with 5-minute expirations.

Question 7

Your team is developing an AI solution that uses Azure Cognitive Services. You need to ensure that the API keys cannot be extracted from the client-side mobile application. What is the recommended architectural pattern to achieve this?

Accepted Answer

Implement a backend API proxy (e.g., Azure Functions or API Management) that authenticates mobile clients and calls Cognitive Services using secure server-side keys. A backend API proxy (Azure Functions or API Management) is the recommended pattern because the Cognitive Services subscription key stays server-side and is never shipped in the mobile binary, where it could be decompiled or intercepted. The mobile client authenticates to the proxy (e.g., via OAuth 2.0/Entra ID token), and the proxy validates that identity before calling Cognitive Services with the secret key. This also enables rate limiting, key rotation, and per-user quota enforcement without redeploying the app.

Answer

Use Azure Front Door to strip API keys from incoming mobile requests

Answer

Store the Cognitive Services API key in an encrypted local SQLite database on the mobile device

Answer

Configure Cognitive Services for anonymous public access and rely on mobile app version checks

Question 8

When storing vector embeddings in Azure Cosmos DB for MongoDB (vCore), which distance metric is natively supported when creating a vector search index?

Accepted Answer

Cosine distance (cos). Azure Cosmos DB for MongoDB (vCore) natively supports cosine distance when creating a vector index, specified as 'cos' in the index definition. Cosine distance measures the angle between vectors and is the standard metric for text embeddings, which are typically normalized. This makes it the correct native metric for vector search indexes in that service.

Answer

Levenshtein distance

Answer

Jaccard distance

Answer

Hamming distance

Question 9

Your containerized AI application requires a specific version of a kernel driver that is not installed on the AKS nodes. What is the most efficient way to manage this?

Accepted Answer

Use a privileged InitContainer to load the driver.. A privileged InitContainer runs before the main application container and, with privileged security context, can load kernel modules (e.g., via modprobe or insmod) onto the node. This is the standard Kubernetes pattern for node-level setup that must complete before the workload starts. It is more efficient than rebuilding the cluster and avoids granting the application container itself elevated privileges.

Answer

Mount the /proc filesystem as read-write.

Answer

Run the application as root.

Answer

Rebuild the AKS cluster.

Question 10

You are setting up auto-scaling for a containerized AI web app in Azure Container Apps. You want to scale based on the number of concurrent HTTP requests. Which scaler do you use?

Accepted Answer

HTTP scaler.. The HTTP scaler in Azure Container Apps scales replicas based on the number of concurrent HTTP requests, which directly matches the requirement to scale on concurrent HTTP request count. It is a KEDA-based scaler configured in the container app's scale rules with a `concurrentRequests` threshold.

Answer

Azure Service Bus scaler.

Answer

CPU usage scaler.

Answer

Memory usage scaler.