Free Practice Questions for the Microsoft Certified: Azure AI Cloud Developer Associate AI-200 Exam (2026 Updated)

At Marks4sure, we are dedicated to providing IT professionals with the most accurate and reliable preparation materials for the Microsoft AI-200 exam. To support your certification journey, we have made a selection of our premium 2026 Microsoft Certified: Azure AI Cloud Developer Associate practice questions and answers available completely free. You can take this practice test as many times as you need. Every question includes a detailed, expertly verified explanation to ensure you fully grasp the core security concepts before test day.

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Questions 4

You are developing an AI application that retrieves database credentials from Key Vault by using the Azure SDK for Python.

The application must use managed identity for authentication.

AI-200 Question 4

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

AI-200 Question 4

Options:

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Questions 5

You are designing an Azure Function app that exposes a public API.

The solution must:

Validate incoming request data and return results immediately to the caller.

Support Microsoft Entra ID authentication.

Scale automatically under variable load.

You need to implement a trigger.

Which trigger should you implement?

Options:

HTTP

Azure Queue storage

Azure Event Grid

Service Bus topic

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Questions 6

You have an Azure subscription named Sub1 that contains a resource group named RG1 and a Service Bus queue named SB1.

You plan to implement an Azure Event Grid push even: subscription that will deliver an event lo SB1 whenever a resource is created, modified, or deleted in RG1. You must minimize the development and configuration efforts.

You need to create an Event Grid topic for your planned implementation

Which type of event topic should you create?

Options:

custom

system

event domain

names pact

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Questions 7

You plan to deploy an Azure Container Apps app named App1. App1 will use an access key to connect to a backend API.

The solution must store the key outside of the App1 environment and minimize maintenance efforts.

You need to configure secure key storage for App1.

Which configurations should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

AI-200 Question 7

Options:

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Questions 8

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 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 result codes are sorted alphabetically.

Does the solution meet the goal?

Options:

Yes

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Questions 9

You are developing several microservices to run on Azure Container Apps.

You need to monitor and diagnose the microservices.

Which features should you use? To answer, select the appropriate feature in the answer area.

NOTE: Each correct selection is worth one point.

AI-200 Question 9

Options:

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Questions 10

You have an Azure Service Bus namespace with a partitioned queue named queue1.

You plan to send a large number of messages through queue1 over the next few weeks. The order of messages will be random. You must minimize the possibility of message transmission interruption by transient failures of individual partitions.

You need to use the optimal configuration of the partition key in the messages.

Which configuration should you use?

Options:

Set the partition key of messages to the message ID value.

Enable sessions. Ensure that the partition key is different from the session ID value.

Enable sessions. Set the partition key of messages to the session ID value.

Leave the partition key value as null.

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Questions 11

You are configuring sampling for a distributed application that sends traces to Azure Monitor. The solution must:

• Preserve upstream sampling decisions across distributed traces

• Capture all spans during local testing.

• Sample 10 percent of traces in production.

You need to apply the appropriate sampling configuration for each requirement.

What should you do? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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.

AI-200 Question 11

Options:

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Questions 12

You need to implement trace correlation according to the business requirements.

Which three 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.

NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you se lect.

AI-200 Question 12

Options:

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Questions 13

You need to troubleshoot connectivity failures between microservices running in AKS.

Which troubleshooting actions should you perform? To answer, move the appropriate action to the correct troubleshooting scenario. 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.

AI-200 Question 13

Options:

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Questions 14

You need to configure a connection string for the partner-facing service according to the technical requirements.

What should you use?

Options:

Azure Key Vault references in App Service settings

GitHub secrets

Azure Container Registry Helm chart package

Dockerfile ENV instructions

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Questions 15

You need to configure image build automation based on the technical requirements.

Which settings should you configure? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

AI-200 Question 15

Options:

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Questions 16

You need to configure vector embedding updates according to the business and technical requirements.

Which information should you use? To answer, select the appropriate option in the answer area.

NOTE: Each correct selection is worth one point.

AI-200 Question 16

Options:

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Questions 17

You need to address the known issue resulting from vector similarity queries.

Which two actions should you perform? Each correct answer presents part of the solution. Choose two.

NOTE: Each correct selection is worth one point.

Options:

Add a composite index on the vector fields and metadata properties of the container.

Reduce the numeric precision of the vector embeddings where supported.

Set the account consistency level to Strong.

Change the vector index type from flat to quantizedFlat or diskANN.

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Questions 18

You need to implement the semantic retrieval workflow for the recommendation engine to meet the technical and performance requirements of Fabrikam Inc.

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.

AI-200 Question 18

Options:

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Questions 19

You need to deploy Azure function resources and apps by using an automated, version-controlled CI/CD pipeline that supports declarative infrastructure deployment. What should you use?

Options:

A.
Local Git deployment

B.
GitHub Actions

C.
Azure Functions Core Tools

D.
Azure CLI

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Answer:
B
Explanation:

Detailed Explanation: GitHub Actions is the correct orchestration mechanism among the options because the case requires an automated, version-controlled pipeline and Bicep-based declarative deployment. Microsoft documents GitHub Actions as a supported way to deploy Bicep and automate Azure resource deployment. Local Git, Azure Functions Core Tools, and Azure CLI can participate in development or scripted deployment, but they do not by themselves satisfy the stated requirement for a repository-driven, repeatable CI/CD pipeline.
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 | Deploy Bicep with GitHub Actions | Automate Function app resource deployment

Questions 20

You need to deploy a batch embedding workload according to the planned application architecture.
What should you use?

Options:

A.
YAML-formatted files

B.
kubectl run and create commands

C.
XML-formatted files

D.
az aks commands

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Answer:
A
Explanation:

The batch embedding workload is planned to run in Azure Kubernetes Service (AKS) , and Kubernetes workloads should be defined declaratively by using YAML manifest files . Microsoft states that a Kubernetes manifest defines the desired state of cluster resources, including Deployments, Jobs, Services, ConfigMaps, and other Kubernetes objects. These manifests can then be deployed by using kubectl apply -f < manifest > .yaml.
For this case, YAML is particularly appropriate because the technical requirements emphasize repeatable, version-controlled, automated deployments . A batch vector embedding regeneration process would typically be represented as a Kubernetes workload such as a Job or another batch-oriented resource, with its configuration committed to Git alongside the rest of the deployment definitions.
kubectl run and kubectl create are imperative commands and are less appropriate for a controlled Git-based deployment model because they do not inherently preserve the complete desired-state definition as source-controlled configuration. XML is not the standard Kubernetes manifest format. az aks commands are primarily used to provision and manage the AKS cluster itself rather than declaratively define application workloads running inside the cluster.
Therefore, the correct choice is YAML-formatted Kubernetes manifest files .
Study Guide references: Azure Kubernetes Service → Kubernetes manifests; declarative deployments; kubectl apply; Git-based workload configuration.

Questions 21

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?

Options:

A.
Connection policy

B.
Feed options

C.
Document response

D.
Request options

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Answer:
C

Questions 22

You need to optimize secure database connectivity from the containerized Recommendation API.
How should you configure the application?

Options:

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Answer:

Answer:

Explanation:

Comply with the authentication policy for database access: Use managed identity authentication.
Support high-concurrency requests with minimal latency: Use a connection pooling library.
Protect database stability during traffic spikes: Configure maximum pool size.
The Recommendation API should use managed identity authentication because Fabrikam explicitly requires service-to-database authentication without long-lived credentials. Azure Container Apps managed identities allow applications to obtain Microsoft Entra tokens at runtime and access supported Azure resources without storing usernames, passwords, or connection secrets in application configuration.
For high-concurrency PostgreSQL access, implement a connection pooling library . Azure Database for PostgreSQL documentation recommends connection pooling because repeatedly opening new database connections creates backend processes and consumes CPU and memory. Reusing existing connections reduces connection-establishment overhead and improves latency and throughput. Azure also provides PgBouncer as a built-in pooling option for Flexible Server.
To protect PostgreSQL during sudden traffic spikes, configure a maximum pool size . An unlimited pool can allow application demand to create excessive concurrent connections, which can exhaust database memory and increase contention. Microsoft warns that increasing connection counts can cause significant performance degradation and recommends conservative connection limits combined with pooling.
Therefore, the optimal configuration combines managed identity + connection pooling + bounded pool size .
Study Guide references: Azure Container Apps managed identities; Azure Database for PostgreSQL connection management; PgBouncer; connection-pool sizing and resource protection.

Questions 23

You need to improve throughput for concurrent application requests to PostgreSQL.
What should you implement?

Options:

A.
Implement connection pooling.

B.
Increase shared_buffers.

C.
Enable read replicas.

D.
Increase max_connections.

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Answer:
A
Explanation:

The correct solution is to implement connection pooling , ideally by using the built-in PgBouncer capability in Azure Database for PostgreSQL Flexible Server. Connection pooling allows many concurrent application requests to share and reuse a smaller set of established PostgreSQL server connections instead of repeatedly opening and closing database sessions. Microsoft notes that creating a new PostgreSQL connection for each operation consumes server resources because each connection involves its own backend process. Reusing pooled connections therefore reduces connection overhead, lowers CPU and memory consumption, and improves throughput for high-concurrency workloads.
This directly matches Fabrikam ' s stated requirement that database connections support high concurrency with minimal latency through connection optimization . PgBouncer can efficiently multiplex large numbers of client connections onto fewer PostgreSQL connections, particularly in transaction pooling mode.
Increasing max_connections is not the preferred solution because each additional PostgreSQL connection consumes memory and other resources; Microsoft explicitly recommends PgBouncer instead when more concurrent connections are required. Increasing shared_buffers addresses caching and memory allocation rather than connection-management overhead. Read replicas can scale read workloads, but they do not optimize the application ' s connection lifecycle.
Study Guide references: Azure Database for PostgreSQL Flexible Server → connection pooling; PgBouncer; connection management and high-concurrency performance.

Questions 24

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.

Options:

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Answer:

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.

Questions 25

You are implementing the expiration requirement for AI-generated summaries in the Redis cache.
You need to purge the AI summaries five minutes after creation, regardless of how often they are accessed.
What should you do?

Options:

A.
Set the maxmemory-policy to allkeys-lru

B.
Configure absolute expiration on each cache key

C.
Use the PERSIST command after each successful read operation

D.
Implement sliding expiration on each cache key

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Answer:
B
Explanation:

Configure a fixed expiration timeout on each Redis key when the summary is created , such as a five-minute TTL. Azure Managed Redis automatically removes a key when its configured timeout expires. Critically, ordinary read operations do not extend that expiration period, so a summary created at time T will expire approximately five minutes later regardless of how frequently clients retrieve it.
This is the required absolute-expiration behavior. In Redis, applications can establish expiration when writing the value or by using expiration commands after creation. The important design property is that the expiration deadline is not refreshed whenever the key is read.
An allkeys-lru eviction policy addresses memory pressure , not deterministic five-minute expiration. It may evict less recently used keys when memory is constrained, but it does not guarantee removal exactly five minutes after creation. The PERSIST command is directly incorrect because it removes an existing timeout , potentially causing the summary to remain indefinitely. Sliding expiration is also unsuitable because it renews or resets the expiration window based on activity, allowing frequently accessed summaries to remain longer than five minutes.
Therefore, a per-key absolute five-minute expiration is the correct configuration.
Study Guide references: Azure Managed Redis → key expiration; TTL behavior; cache expiration versus eviction; fixed versus sliding expiration.

Questions 26

You are troubleshooting a production Azure OpenAI service that is experiencing intermittent connectivity issues. All telemetry is sent to a Log Analytics workspace.
You must write a KQL query that analyzes the AppRequests table. The query must filter for requests that occurred within the last 30 minutes and have a failed status.
The final output must display the total number of failures for each unique operation.
You need to select the KQL operators required to filter the time range and aggregate the failure counts.
Which two operators should you use? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.

Options:

A.
project

B.
distinct

C.
where

D.
summarize

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Answer:
C, D

Questions 27

You need to configure the Redis integration for the Recommendation API.
Which configurations should you use? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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.

Options:

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Answer:

Answer:

Explanation:

Comply with the defined expiration policy for recommendation results: Configure Time to Live on each cache key.
Support the data integrity and freshness requirement for catalog updates: Publish invalidation events to a Redis channel.
Meet the search latency target for recurring product requests: Implement cache-aside pattern with lazy loading.
The 10-minute expiration requirement should be implemented by configuring a Time to Live (TTL) on each Redis cache key. Redis automatically removes a key after its assigned expiration interval, ensuring recommendation results cannot remain cached indefinitely. Microsoft documents expiration as a standard mechanism for preventing cached data from becoming stale.
For catalog changes, Fabrikam explicitly requires a reactive invalidation mechanism . Publishing invalidation events to a Redis Pub/Sub channel allows application components to notify subscribers immediately when product metadata or pricing changes. Redis Pub/Sub broadcasts messages to channel subscribers, which can then invalidate affected cache entries instead of waiting for TTL expiration.
For repeated recommendation queries, implement the cache-aside pattern with lazy loading . The application first checks Redis; on a cache hit, results are returned from the low-latency in-memory cache. On a miss, the application retrieves data from PostgreSQL, places the result in Redis, and returns it. Microsoft identifies cache-aside as an appropriate pattern for frequently read data and explicitly notes that Azure Managed Redis provides low-latency, high-throughput access.
Redis RDB persistence is unnecessary for these requirements because the cache is an optimization layer rather than the authoritative data store.
Study Guide references: Azure Managed Redis → cache-aside pattern; TTL/expiration; Pub/Sub; cache invalidation and data freshness.

Questions 28

You need to configure the database resources for the Azure Database for PostgreSQL instance.
How should you complete the configuration to meet the business and technical requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Options:

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Answer:

Answer:

Explanation:

Meet the 200-ms semantic search latency requirement: Increase compute vCores.
Optimize the environment for high-dimensional pgvector index residency: Increase memory allocation.
Support the continuous ingestion of transaction-based embeddings: Enable storage autoscale.
For the strict sub-200-ms vector-search latency target, increasing compute vCores is the appropriate choice. Vector similarity operations are computationally intensive, and additional CPU capacity improves mathematical throughput and parallel query execution. Microsoft’s pgvector guidance emphasizes query-plan optimization, ANN indexes such as HNSW, and sufficient compute resources when optimizing vector workloads.
For high-dimensional pgvector index residency , increase memory allocation . HNSW provides strong query-performance characteristics but consumes more memory than IVFFlat. Keeping frequently accessed vector index structures in memory minimizes disk access and materially improves latency. Microsoft explicitly notes that HNSW requires more memory while providing a better speed/recall tradeoff.
For the continuous ingestion of millions of embeddings, enable storage autoscale . Azure Database for PostgreSQL Flexible Server can automatically increase allocated storage as capacity approaches configured thresholds, avoiding an out-of-storage condition as data volumes grow. Microsoft recommends storage autogrow for workloads whose storage demand can increase dynamically.
Increasing max_connections does not directly improve vector computation or index residency, read replicas primarily scale reads, and backup retention does not address ingestion capacity.
Study Guide references: Azure Database for PostgreSQL Flexible Server → pgvector performance optimization; compute and memory sizing; HNSW indexing; storage autogrow.

Exam Code: AI-200

Exam Name: Developing AI Cloud Solutions on Azure

Last Update: Sep 20, 2026

Questions: 142

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