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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Topic 1: Implement Azure AI solutions- Implement computer vision solutions
- Implement knowledge mining with Azure AI Search
- Implement generative AI solutions using Azure OpenAI
- Implement natural language processing solutions
Topic 2: Implement and monitor AI workloads- Monitor performance and troubleshoot issues
- Deploy AI models and services
Topic 3: Plan and manage Azure AI solutions- Select appropriate Azure AI services
- Monitor and optimize AI solutions
- Plan security and compliance requirements

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You are preparing a production deployment for an Azure Function app. The app will run across multiple environments.
The solution must support environment-specific configuration and prevent secrets from being stored in source control.
You need to develop the solution.
Solution: Store connection strings in the Function app application settings configured in the Azure Portal.
Does the solution meet the goal?

  • A. Yes
  • B. No
Answer: A

Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

A company uses Azure Monitor Application Insights to monitor application behavior, including incoming requests and dependencies.
You must identify failed requests from the last hour. You must also calculate the average duration of failed request dependency calls, grouped by operation name.
You need to analyze telemetry in Application insights.
Which operators should you mm? To answer, select the appropriate options in the answer area NOTE: Each cot reel selection is worth one point.

Answer:


Explanation:

You plan to create a Docker image that runs an ASP.NET Core application named ContosoApp. You have a setup script named setupScript.psl and a series of application files including ContoscApp.dll.
You need to create a Dockerfile document that meets the following requirements:
* Call SQiupScript.psI when the container is built.
* Run CantosoApp.dill when the container starts.
The Dockerfile document must be created in the same folder where ContosoApp.dll and setupScnpt.ps! are stored.

Answer:


Explanation:

You are developing a Retrieval-Augmented Generation (RAG) solution for a company. AI responses and embedding vectors are cached in Redis. The solution must meet the following requirements.
* AI responses must expire exactly 24 hours after they are cached
* Cached embeddings must always reflect the current source data.
You need to configure Redis to meet the requirements.

Answer:


Explanation:

I response expiry: set a 24-hour TTL on each key. Embedding freshness: delete related cache keys when the source document changes.
Detailed Explanation: A fixed TTL encodes the requirement that an AI response must expire after a defined period rather than survive indefinitely or have its lifetime extended on every read. Embedding freshness is a separate consistency requirement: when the authoritative document changes, any cached representation derived from the old document must be invalidated immediately. Memory eviction policies are capacity- management mechanisms and cannot guarantee a precise 24-hour lifetime or source-data consistency.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Azure Managed Redis documentation

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:
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

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