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| Section | Objectives |
|---|---|
| Topic 1: Implement Azure AI solutions | - Implement natural language processing solutions - Implement computer vision solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI |
| Topic 2: Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Plan security and compliance requirements - Monitor and optimize AI solutions |
| Topic 3: Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
>> AI-200 Actual Test Answers <<
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NEW QUESTION # 107
Hotspot Question
You plan to configure an Event Grid subscription for AI inference events.
The solution must:
- Filter events by payload data.
- Retain undelivered events.
- Limit retry attempts.
You need to configure the Event Grid.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Advanced filter
You must use an Advanced filter to filter Azure Event Grid events by payload data.
Payload-level access: Advanced filters allow you to filter on fields within the data object of the event schema, which contains the specific payload of your AI inference event.
Complex operators: It supports advanced comparisons like StringContains, NumberGreaterThan, or BoolEquals against specific JSON payload paths.
Box 2: Dead-letter destination
You should use a dead-letter destination because it is the native Azure mechanism designed to retain undelivered events when delivery attempts fail or are dropped.
Purpose: Captures events that cannot be delivered to your AI inference endpoint due to errors, timeouts, or bad payloads.
Storage Requirement: Requires an Azure Storage account container to securely hold the undelivered message blobs.
Retention Policy: Events stored in the dead-letter container are retained for 14 days by default, giving you time to investigate.
Box 3: Maximum delivery attempts
Maximum delivery attempts is the feature to use to limit retry attempts in an Event Grid subscription.
Use this setting to set a hard cap on how many times Event Grid tries to send an event. You can lower this number from the default value to stop retrying sooner.
Reference:
https://learn.microsoft.com/en-us/azure/data-manager-for-agri/how-to-use-events
https://learn.microsoft.com/en-us/azure/event-grid/subscriber-operations
NEW QUESTION # 108
Drag and Drop Question
You are preparing a container image for deployment to production.
The container image build and deployment process must ensure the following:
- The image is uniquely versioned.
- Secure authentication is used when pushing the image to Azure
Container Registry (ACR).
- The image is stored in ACR for deployment.
You need to ensure that the container image build-and-push process meets the requirements.
Which action should you perform for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: use a unique tag for the image
You should use a unique tag-such as the Git commit SHA or a semantic version combined with a build number-for each container image deployed to production.
Using unique tags ensures that every deployment is traceable, repeatable, and easily rollable back to a specific, immutable state.
Box 2: Authenticate to ACR by using Microsoft Entra ID
You should authenticate to Azure Container Registry (ACR) using Microsoft Entra ID to ensure secure, role-based, and auditable authentication for production deployments.
Eliminates Static Credentials: It replaces long-lived, risky passwords or registry admin keys with short-lived, automatically rotated tokens.
Granular Access Control: It leverages Azure Role-Based Access Control (Azure RBAC). You can assign the specific AcrPush role to your deployment identity, satisfying the principle of least privilege.
Auditability: Every login and image push action is tied to a specific identity and logged, which is critical for production compliance.
Box 3: Push the image to ACR
Pushing the built image to ACR satisfies the exact requirement of storing the container image in a secure, private registry for cloud deployment.
Reference:
https://learn.microsoft.com/en-us/azure/container-registry/container-registry-authentication
NEW QUESTION # 109
You are developing a microservices-based application that uses Azure Container Apps The application consists of several containerized services that handle tasks, such as processing orders, managing inventory, and generating reports.
You must secure the container apps. All apps must reside in the same virtual network, share the same Dapr configuration, and share the same logging location.
Apps must support the configuration of the amount of memory and compute resources available to containers.
You need to configure the Azure Container App
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified answer: `az containerapp env create ... --enable-workload-profiles`.
Detailed Explanation: The shared boundary for Container Apps is the managed environment. Apps in one environment can share the environment's virtual network integration, observability destination, and Dapr- related environment configuration. Enabling workload profiles provides selectable compute profiles and resource sizing options for workloads in that environment. The CLI must therefore create a Container Apps environment and enable workload profiles. Creating an individual container app alone would not establish the common environment-level network, logging, and resource-profile boundary required by the scenario.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Azure Container Apps environments
NEW QUESTION # 110
A Python API running in ACA must send distributed traces to Azure Monitor.
The API creates spans. However, no traces appear in Azure Monitor.
You need to configure the OpenTelemetry SDK pipeline to export traces to Azure Monitor.
What should you do? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: Register a global trace provider # initialize the application TraceProvider. Export traces to Azure Monitor # create/configure AzureMonitorTraceExporter. Connect exporter to provider # add a span processor. Generate spans # use tracer.start_as_current_span(...).
Detailed Explanation: OpenTelemetry tracing is a pipeline: the application obtains a tracer from a configured provider, creates spans, passes ended spans through a span processor, and exports them using the Azure Monitor exporter. Creating spans alone is insufficient; without the provider/processor/exporter chain, no telemetry reaches Azure Monitor. Log sampling and legacy Application Insights TrackEvent calls are unrelated to the required OpenTelemetry trace-export path.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Enable Azure Monitor OpenTelemetry
NEW QUESTION # 111
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must retrieve the five most similar documents to an input embedding.
You need to construct the similarity query.
What should you do?
Answer: D
Explanation:
To retrieve the five most similar documents from Azure Cosmos DB for NoSQL using vector search, you must limit the number of results.
When performing vector similarity searches using the VectorDistance() function, you must restrict the output size directly in the query.
Reference:
https://stemhash.com/semantic-search-with-llms/
NEW QUESTION # 112
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