- Exam Code: AI-200
- Exam Name: Developing AI Cloud Solutions on Azure
- Updated: Sep 27, 2026
- Q & A: 144 Questions and Answers
Microsoft Developing AI Cloud Solutions on Azure : AI-200 valid dump
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Microsoft AI-200 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
| Topic 2: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Topic 3: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 4: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
Question #1
An AI platform uses App Configuration for feature flags and endpoint routing.
The platform stores secrets alongside configuration data and does NOT support dynamic configuration refresh.
You need to redesign the configuration approach so that:
configuration supports dynamic refresh;
secrets are stored outside App Configuration;
the application can securely access both App Configuration and Key Vault.
You need to enable secure dynamic configuration management for the platform.
Which three actions should you perform? Each correct answer presents part of the solution. Choose three.
NOTE: Each correct selection is worth one point.
- A. Use managed identity for both App Configuration and Key Vault access.
- B. Store configuration values in environment variables.
- C. Store API keys in Key Vault.
- D. Allow configuration updates with a polling interval.
- E. Use a service principal secret for both App Configuration and Key Vault access.
Correct Answer: A,C,D 🗳️
Explanation: Only visible for Actual4Dumps members. You can sign-up / login (it's free).
Question #2
You plan to deploy a web application to AKS.
The solution must:
* Scale out the application by adding more pods during peak CPU usage
* Expose the application internally within the cluster only
You need to configure a Kubernetes resource for each requirement
Which resources should you configure? To answer, move the appropriate resources to the correct requirements You may use each resource once, more than once, or not at all. You may need to move split bar between .. or scroll to view content.
Correct Answer:
Explanation:
Verified Answer: Scale out the application: HorizontalPodAutoscaler. Expose internally only: ClusterIP service.
Detailed Explanation: The Kubernetes HorizontalPodAutoscaler increases or decreases pod replicas in response to CPU, memory, or other metrics, so it is the resource that implements CPU-driven pod scale-out. A ClusterIP service creates an internal service IP that is reachable within the Kubernetes cluster and is the normal choice for an internal-only service. A Deployment defines the workload but does not itself implement metric-driven autoscaling, while Ingress is intended to route inbound network traffic.
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 | AKS scaling overview | Kubernetes services in AKS
Question #3
You are designing a solution that will use two Azure Functions apps: App1 and App2. App1 is Windows based and will be deployed as code. App2 is Linux based and will be deployed as a container image.
Estimates show that the duration of the request processing for both apps will range from 1 to 10 minutes.
You plan to implement App1 and App2 by using the hosting plan to satisfy the following requirements:
* Request processing can complete within the estimated time range.
* The autoscaling behavior is event driven.
* The upper scaling limit is maximized.
You need to create the hosting plan for the implementation.
Which hosting plan should you create? To answer, move the appropriate hosting plans to the correct apps.
You may use each hosting plan once, more than once, or not at all. You may..
Correct Answer:
Explanation:
pp1: Consumption. App2: Premium.
Detailed Explanation: For App1, the Windows code-based Consumption plan satisfies event-driven autoscaling, allows execution up to the stated ten-minute range, and has the highest scaling ceiling among the listed event-driven choices for this workload. App2 is deployed as a Linux container image; among Premium, Dedicated, and Consumption, Premium is the event-driven plan that supports Linux containerized Functions and permits long-running execution. Dedicated hosting is not the requested event-driven autoscale model. The original Premium/Premium answer therefore did not maximize App1's upper scaling limit.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Azure Functions scale and hosting
Question #4
You are developing a Python application that reads data from Azure Cosmos DB for NoSQL by using the azure-cosmos SDK.
You need to connect to an existing account and run a SQL query against an existing container.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
* Initialize CosmosClient.
* Specify the database name.
* Obtain the container reference.
* Run the query.
The Python Azure Cosmos DB SDK follows the resource hierarchy Account # Database # Container # Items . The first step is to initialize a reusable CosmosClient with the Azure Cosmos DB account endpoint and an appropriate credential. Microsoft identifies CosmosClient as the primary client for interacting with databases in an account.
Next, specify the existing database by calling client.get_database_client(database_name). This returns a DatabaseProxy representing that database. Importantly, DatabaseProxy should not be instantiated directly ; Microsoft explicitly states that it should be obtained through CosmosClient.get_database_client().
From the database reference, obtain the target container by using database.get_container_client (container_name). Microsoft documents this as the standard way to retrieve an existing container reference.
Finally, execute the SQL query through container.query_items() , which accepts the Cosmos DB for NoSQL query and optional parameters and returns an iterable result set.
Therefore, Initialize DatabaseProxy is the distractor: the SDK creates the DatabaseProxy through get_database_client; application code should not construct it directly.
Study Guide references: Azure Cosmos DB for NoSQL # Python SDK; CosmosClient; database and container clients; ContainerProxy.query_items().
Question #5
You are developing a solution for a hospital to support the following use cases:
*The most recent patient status details must be retrieved even if multiple users in different locations have updated the patient record.
*Patient health monitoring data retrieved must be the current version or the prior version.
*After a patient is discharged and all charges have been assessed, the patient billing record contains the final charges.
You provision a Cosmos DB NoSQL database and set the default consistency level for the database account to Strong. You set the value for Indexing Mode to Consistent.
You need to minimize latency and any impact to the availability of the solution. You must override the default consistency level at the query level to meet the required consistency guarantees for the scenarios.
Which consistency levels should you implement? To answer, drag the appropriate consistency levels to the correct requirements. Each consistency level may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Strong consistency is required for the patient-status scenario because it provides linearizability : reads return the latest committed value. This is the appropriate guarantee when updates can originate from different locations and the application must never return an older patient status. Microsoft defines Strong as the highest Azure Cosmos DB consistency level and notes that it provides the strongest freshness guarantee, at the cost of greater latency and reduced availability compared with weaker models.
For health-monitoring data, Bounded Staleness is the intended consistency level. It explicitly limits how far reads can lag behind writes using a maximum number of versions (K) or a time interval (T). Conceptually, this matches a requirement that data may be current or only a limited number of versions behind.
For finalized billing data, Eventual consistency provides the lowest latency and highest availability. Once discharge processing is complete and no further billing writes occur , replicas eventually converge on the same final value. Microsoft specifically notes that, in the absence of further writes, weaker consistency levels ultimately yield the same converged data.
Azure Cosmos DB also permits requests to relax consistency below an account ' s Strong default , which is exactly the optimization pattern described here.
Study Guide references: Azure Cosmos DB for NoSQL # Strong consistency; Bounded Staleness; Eventual consistency; per-request consistency override; latency/availability trade-offs.
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