About the Exam

Microsoft exam for the Azure AI Cloud Developer Associate certification. It covers designing, developing, deploying and monitoring AI-powered cloud solutions on Azure, including serverless inference endpoints, asynchronous AI processing workflows, semantic retrieval with vector databases, and the monitoring of AI solution health and performance. Passing demonstrates the ability to build and operate AI applications on Azure at scale.

Exam Topics

Develop containerized solutions on Azure20–25%
Develop AI solutions by using Azure data management services25–30%
Connect to and consume Azure services20–25%
Secure, monitor, troubleshoot Azure solutions20–25%

How to Use This Practice Exam

Browse — Read each question, select your answer, and reveal the explanation.
Exam Mode — Simulate real exam conditions with a timed session and score report.
Learn Mode — Spaced repetition schedules questions you struggle with for long-term retention.

Download the Full Exam PDF

Get every question and answer in a clean, printable PDF built for offline study. Purchase once, keep permanent access, and re-download the latest version anytime.

Get PDF

Learn More

Last updated July 20, 2026 at 6:40 PM

Topic filter

Retired questions

Question sort

QuestionQ1

Develop containerized solutions on Azure

You manage several versions of a container image in Azure Container Registry.

The production deployment must consistently run the identical image build, even if tags change later.

You need to guarantee predictable, immutable image selection during deployment. What should you do?

A Tag the image as production and deploy it by using the production tag.
B Schedule nightly rebuilds of the image.
C Configure deployment to use the latest tag.
D Identify the image by using its SHA digest.

Explanation

A container image manifest digest is a unique SHA-256 content identifier for that exact image build. Deploying by digest pins the deployment to that image even if a tag such as production or latest is later moved to another build. About Registries, Repositories, Images, and Artifacts – Azure Container Registry

Learn more

About Registries, Repositories, Images, and Artifacts – Azure Container Registry

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ2

Secure, monitor, troubleshoot Azure solutions

A container in an AKS cluster is restarting repeatedly.

Pod events indicate probe failures, while node-level CPU and memory metrics are normal.

You need to identify the cause of the recurring restarts. What should you do first?

A Scale the deployment to more replicas.
B Decrease the initialDelaySeconds for the container liveness probe.
C Drain and reboot the node hosting the pod.
D Inspect the pod events and container logs.

Explanation

Liveness or readiness probe failures can cause recurring container restarts even when node resources are healthy. Pod events provide the probe failure details, and container logs—especially logs from the previous container instance—expose application startup, endpoint, and termination errors needed to identify the root cause.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ3

Develop containerized solutions on Azure

You develop a message-processing service that is deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.

The solution must minimize costs by ensuring that no compute resources are consumed when the queue is empty.

You need to configure scaling for the service.

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

> NOTE: Each correct selection is worth one point.

Choose two

A Increase the scaling rule to allow for the maximum running replica count.
B Configure the scaling rule to allow for the termination of all active replicas.
C Configure a Kubernetes Event-driven Autoscaler rule that monitors queue length.
D Enable HTTP ingress concurrency scaling.

Explanation

Azure Container Apps uses KEDA-based custom scaling rules to scale on Azure Service Bus queue messages. Configuring a KEDA rule that monitors queue length starts replicas when messages are pending, and allowing all replicas to terminate permits the app to scale to zero when the queue is empty, eliminating idle compute consumption.

Learn more

Scaling in Azure Container Apps

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ4

Develop containerized solutions on Azure

You configure ACR Tasks to automate container image builds.

Container images must be rebuilt when:

Application updates occur.
Base image updates occur, such as when the underlying OS image is updated.
Regular scheduled rebuilds are needed.

You need to configure ACR Tasks to support automated image rebuilds.

Which three triggers should you configure? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

Choose three

A Timer trigger
B Source code commit trigger
C Registry event trigger
D Base image update trigger
E Webhook notification trigger

Explanation

Azure Container Registry Tasks can automatically run builds on source-code commits, base-image updates, and timer schedules. These triggers respectively handle application changes, updated parent images such as OS images, and recurring rebuilds.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ5

Develop containerized solutions on Azure

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

The microservices must permit 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?

Drag & Drop

Add the custom domain name.

Bind the certificate.

Enable ingress.

Validate the custom domain name.

Add DNS records to the domain provider.

Explanation

Azure Container Apps requires ingress for a custom-domain endpoint. Adding the domain provides the values used to create the required DNS ownership and routing records. Azure can validate the domain only after those records resolve, and a TLS certificate is bound after validation succeeds to enable HTTPS.

Learn more

Custom domain names and free managed certificates in Azure Container Apps

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ6

Connect to and consume Azure services

Background

Fabrikam Inc. is a global retail analytics company that delivers AI-driven demand forecasting and product recommendation services to online retailers. The company is modernizing its solution to run entirely on Microsoft Azure.

The platform ingests transaction data, creates embeddings for semantic retrieval, performs vector similarity search, and returns product recommendations through containerized microservices. Developers use Python and Azure SDKs. Operations teams manage container orchestration, scaling, monitoring, and security.

The solution must satisfy strict performance, scalability, and security requirements.

Current environment

Application architecture

The Recommendation engine is a customer-facing HTTP API that runs as a containerized Python application.
The engine is deployed to Azure Container Apps (ACA).
Embeddings are stored in Azure Database for PostgreSQL by using pgvector.
Semantic retrieval uses metadata filtering together with vector similarity search.
Azure Managed Redis is used as a caching layer.
Front-end and API workloads are deployed to Azure Container Apps (ACA).
Batch model retraining workloads run in Azure Kubernetes Service (AKS).

Container and CI/CD

Container images are stored in Azure Container Registry (ACR).
CI/CD uses ACR Tasks to build images on commit.
ACA environments support revision management.
AKS workloads are deployed by using Kubernetes manifest files stored in Git.

Monitoring

Logs are collected in Azure Monitor.
Teams inspect container logs and Kubernetes events when troubleshooting.
Developers write KQL queries to analyze latency spikes.

Business requirements

Customer experience: Maintain a seamless, low-latency recommendation experience for end users, even during unpredictable seasonal traffic spikes.
Operational cost efficiency: Minimize compute costs by deallocating resources during periods of inactivity and by preventing runaway scaling costs.
Data integrity and freshness: Ensure that product recommendations always reflect the most current catalog metadata and pricing to prevent customer dissatisfaction.
Security and compliance: Follow a Zero Trust security model by removing long-lived credentials and centralizing the management of all sensitive secrets.
Global scalability: Support the rapid ingestion of millions of new product embeddings daily without degrading query performance for existing retailers.

Technical requirements

Performance: Semantic search latency must stay under 200 milliseconds at peak load.
Database optimization: Use pgvector for embeddings and implement metadata filtering to reduce compute overhead. Configure compute and memory appropriately for vector workloads to ensure high-dimensional index residency in RAM and efficient mathematical throughput. Vector similarity calculations must be performed only against products that satisfy mandatory metadata constraints.
Database performance: Database connections must support high concurrency with minimal latency through the implementation of connection optimization.
Data load strategy: To ensure maximum ingestion throughput, secondary indexes must be applied only after bulk loading of embeddings is complete.
Caching: Redis cache entries must expire automatically after 10 minutes. Implement a reactive mechanism to invalidate cache entries when metadata updates occur.
Identity: Use managed identities for all service-to-service and service-to-database authentication. Plain-text credentials in configuration files are strictly prohibited.
Secret management: All secrets must be stored centrally. Secrets must be rotated automatically by using a centralized lifecycle policy.
Scaling: Use Kubernetes event-driven autoscaling (KEDA) for event-driven scaling. The Recommendation API must scale based on HTTP traffic, while batch jobs must scale based on queue length and support scale-to-zero.
CI/CD: All images must be stored in Azure Container Registry. Use ACR Tasks to automate image builds triggered by source code commits.
Monitoring: Use KQL to analyze performance telemetry and troubleshoot microservice connectivity failures. Inspect logs and events when troubleshooting AKS and ACA.

Back home

AI-200: Developing AI Cloud Solutions on AzureDemo

Exam Topics

How to Use This Practice Exam

Download the Full Exam PDF

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Background

Current environment

Application architecture

Container and CI/CD

Monitoring

Business requirements

Technical requirements

Background

Current environment

Application architecture

Container and CI/CD

Monitoring

Business requirements

Technical requirements

Community Discussion

Background

Current environment

Application architecture

Container and CI/CD

Monitoring

Business requirements

Technical requirements

Community Discussion

Background

Current environment

Application architecture

Container and CI/CD

Monitoring

Business requirements

Technical requirements

Community Discussion

Background

Current environment

Application architecture

Container and CI/CD

Monitoring

Business requirements

Technical requirements

Community Discussion

Background

Current environment

Application architecture

Container and CI/CD

Monitoring

Business requirements

Technical requirements

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Community Discussion

Background

Planned Application Architecture

Monitoring

Business Requirements

Technical Requirements

Known Issues

Community Discussion