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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question 1

Drag and Drop Question
You are developing several microservices to run on Azure Container Apps.
The microservices must allow HTTPS access by using a custom domain.
You need to configure the custom domain in Azure Container Apps.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Question 2

Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
Hotspot Question
You need to configure vector embedding updates according to the business and technical requirements.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question 3

Your Azure AI Search index must support both exact keyword matches on product SKUs and semantic similarity search on product descriptions in a single query. What should you configure?

A. Two completely separate indexes queried independently
B. A keyword-only index
C. A vector-only index
D. A hybrid search index combining vector fields and searchable text fields

Question 4

Drag and Drop Question
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome 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.

Question 5

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. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You are using Azure Monitor Application Insights to investigate a production API. You open the Logs blade and set the time range to Last 24 hours.
An engineer recommends the following query to count requests by result code and sort the results from most frequent to least frequent:
requests
| summarize request_count = count() by resultCode
| order by request_count desc
You need to determine whether the query returns the number of requests grouped by result code and sorted from most to least frequent.
Solution: The query returns one row per unique resultCode value with the number of requests in each group.
Does the solution meet the goal?

A. Yes
B. No

Solutions: