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Microsoft AI-200 Q&A - in .pdf
- Exam Code: AI-200
- Exam Name: Developing AI Cloud Solutions on Azure
- Updated: Sep 18, 2026
- Q & A: 144 Questions and Answers
- Convenient, easy to study.
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- Exam Code: AI-200
- Exam Name: Developing AI Cloud Solutions on Azure
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- Updated: Sep 18, 2026
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- Exam Code: AI-200
- Exam Name: Developing AI Cloud Solutions on Azure
- Updated: Sep 18, 2026
- Q & A: 144 Questions and Answers
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Microsoft AI-200 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
Question #1
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column. The schema must support vector similarity search and reliable date filtering You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
- A. Use a typed timestamp column for created_at.
- B. Store embeddings in a varchar column.
- C. Store embeddings in a pgvector column.
- D. Store created_at as a free-form string.
Correct Answer: A,C 🗳️
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Question #2
You are creating an Azure Functions app project in your local development environment by using Azure Functions Core Tools.
You must create the project in either Python or C# without using a template.
You need to specify the command and its parameter required to create the Azure Functions app project.
Which command and parameter should you specify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* Command: func init
* Parameter: --worker-runtime
Use func init to initialize a new Azure Functions project. Microsoft documents func init as the Azure Functions Core Tools command that creates the project structure in either the current directory or a specified project folder. The command is specifically intended for creating a new Functions application project, whereas func new creates an individual function from a template inside an already initialized project.
The --worker-runtime parameter specifies the language/runtime model for the Functions project. For example, Microsoft documents func init --worker-runtime python for Python projects and func init --worker- runtime dotnet-isolated for modern C#/.NET isolated-worker projects.
--language is not the primary parameter used to establish the Functions worker runtime in this scenario. It can be relevant to certain runtime combinations, such as Node.js, but the Core Tools project initialization examples for Python and C# rely on --worker-runtime. --target-framework is used to select a specific .NET target framework after the worker/runtime model is determined; it does not identify whether the project should be Python or C#.
Therefore, the correct combination is func init + --worker-runtime .
Study Guide references: Azure Functions Core Tools # func init; local Functions project creation; --worker- runtime; Python and .NET isolated worker projects.
Question #3
You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history. The API key must not be stored in application configuration directly. The API key must be accessed securely from Azure Key Vault.
You need to ensure that the API key is stored securely in Azure Key Vault and is available to the container at runtime without being exposed in source control or Git commit history.
Solution: Store the API key as a GitHub repository secret.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
Question #4
You manage an Azure Cosmos DB for a NoSQL API account named account1. The account contains a database named db1, which contains a container named container1. You configure account1 with a session consistency level.
You plan to develop an application named App1 that will access container1. Individual instances of App1 must perform reads and writes. App1 must allow multiple nodes to participate in the same session.
You need to configure an object to share the session token between the nodes.
Which object should you use?
- A. Document response
- B. Request options
- C. Feed options
- D. Connection policy
Correct Answer: A 🗳️
Question #5
You need to deploy a batch retraining workload.
How should you complete the scaling configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* Trigger scaling based on queue depth: Azure Service Bus scaler
* Deallocate instances when the message queue is empty: Minimum replicas = 0
* Prevent uncontrolled burst scaling: Maximum replicas = 10
The batch retraining workload must scale according to queue length , so the correct KEDA trigger is the Azure Service Bus scaler . KEDA integrates with Azure Service Bus and monitors queue or subscription metrics, allowing AKS workloads to scale according to the number of pending messages rather than CPU utilization or HTTP request concurrency. Microsoft documents KEDA as the event-driven autoscaling mechanism for AKS and specifically supports Azure-based scalers for queue-driven workloads.
To meet the requirement to deallocate workload instances when no messages remain, configure minimum replicas = 0 . KEDA supports scale-to-zero for event-driven workloads, which is particularly appropriate for intermittent batch or machine-learning processing because no pods need to remain running while the queue is empty. Microsoft specifically identifies scale-to-zero as a KEDA capability and recommends it for sporadic ML workloads where cost optimization is important.
Finally, configure maximum replicas = 10 . A finite maximum replica count establishes an explicit upper scaling boundary, preventing large queue bursts from creating an uncontrolled number of worker replicas and protecting both infrastructure cost and downstream services.
Study Guide references: AKS # KEDA; Azure Service Bus event-driven scaling; minReplicaCount; maxReplicaCount; scale-to-zero; autoscaling limits.
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