Microsoft · AI-200
Microsoft Certified: Azure AI Cloud Developer Associate (AI-200) Practice Tests
Validates the ability to design, build, and implement AI solutions on Azure with an emphasis on back-end services, scalable architectures, containerized workloads, and the full development lifecycle. Covers Azure SDKs, data management services, messaging and eventing, vector databases, and AI solution monitoring.
Exam Details
Practice Questions
600
≈ 12 practice exams
Duration
120 minutes
Passing Score
700/1000
Difficulty
AssociateLast Updated
Jun 2026
Topics Covered
Develop containerized solutions on AzureDevelop AI solutions using Azure data management servicesConnect to and consume Azure servicesSecure, monitor, and troubleshoot Azure solutionsAzure messaging and eventingVector databases and semantic retrievalPython programming and Azure SDKs
Microsoft Certified: Azure AI Cloud Developer Associate (AI-200) Practice Exam Preparation
Use this AI-200 practice exam to prepare for Microsoft Certified: Azure AI Cloud Developer Associate (AI-200) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for Microsoft AI-200, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Develop containerized solutions on Azure, Develop AI solutions using Azure data management services, Connect to and consume Azure services, Secure, monitor, and troubleshoot Azure solutions, and Azure messaging and eventing. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Develop containerized solutions on Azure23%
Develop AI solutions by using Azure data management services28%
Connect to and consume Azure services23%
Secure, monitor, and troubleshoot Azure solutions23%
Exam Overview
Microsoft Certified: Azure AI Cloud Developer Associate (AI-200) validates a developer's ability to design, build, and implement production-grade AI solutions on Azure with a strong emphasis on back-end services, scalable architectures, and the full development lifecycle. The certification covers a broad set of Azure capabilities including containerized workloads on Azure Container Registry, Azure Container Apps, and Azure Kubernetes Service (AKS); AI-ready data management using Azure Cosmos DB for NoSQL and Azure Database for PostgreSQL with pgvector; event-driven integration through Azure Service Bus and Azure Event Grid; and serverless compute via Azure Functions. It also addresses operational concerns such as secret management with Azure Key Vault, distributed tracing with OpenTelemetry, and log analytics using KQL.
Launched in beta in May 2026 and targeting general availability in July 2026, AI-200 serves as the successor to the retiring AZ-204 exam. It shifts Microsoft's developer certification track toward the skills required to build and operate AI-integrated cloud systems, reflecting enterprise demand for back-end developers who can connect AI models to scalable, observable, and secure cloud infrastructure. Passing the exam also makes candidates eligible to pursue the Azure DevOps Engineer Expert certification.
Who Should Take This Exam
AI-200 is designed for back-end cloud developers who contribute to all phases of implementing AI solutions on Azure — from requirements gathering and design through deployment, security, and monitoring. Ideal candidates are software engineers or cloud developers with hands-on experience building server-side components and integrating Azure services into production systems, typically with 1–3 years of Azure development experience.
The certification is well-suited for developers transitioning from AZ-204, as well as those moving into AI-focused engineering roles where they must connect AI models to data pipelines, messaging systems, and containerized infrastructure. Job titles aligned with this certification include Azure Developer, Cloud Application Developer, AI Solutions Developer, and Backend Software Engineer working within AI product teams.
Prerequisites
Microsoft does not enforce formal prerequisites for AI-200, but candidates are expected to bring meaningful hands-on experience before sitting the exam. Proficiency in Python programming is assumed throughout, as is familiarity with Azure SDKs and common Azure services. Candidates should be comfortable working with containerized applications, including building and pushing container images, and should understand core concepts around REST APIs, cloud storage, and identity management.
A working knowledge of Azure data management services — particularly Azure Cosmos DB and Azure Database for PostgreSQL — is strongly recommended, along with exposure to vector databases and embedding-based retrieval patterns. Developers coming from an AZ-204 background will find many foundational topics carry over, though the AI-200 exam introduces additional depth around vector similarity search, RAG (Retrieval-Augmented Generation) patterns, semantic retrieval, and AI-specific observability tooling.
Exam Format
Exam AI-200 is a proctored assessment delivered online through Pearson VUE, available from a candidate's home or office. Candidates are allotted 120 minutes to complete the exam, and a score of 700 out of 1000 is required to pass. The exam is currently in beta, which means scores are not released immediately — rescoring begins at general availability launch and final results are typically delivered approximately 10 days after the live exam launches.
The exam may include interactive components in addition to standard question types (multiple choice, case studies, drag-and-drop, etc.), which candidates can preview in Microsoft's exam sandbox at https://go.microsoft.com/fwlink/?linkid=2226877. The exam is currently offered in English only, though additional languages may be added post-beta. Microsoft certifications earned at the Associate level expire annually and can be renewed for free via an online assessment on Microsoft Learn. A Practice Assessment is expected to become available within 8 weeks of the exam exiting beta.
Skills Measured
- 1.Develop containerized solutions on Azure (20–25%): Covers building, storing, versioning, and managing container images using Azure Container Registry (ACR) and ACR Tasks; deploying containers to Azure App Service with environment variable and secret configuration; deploying applications to Azure Container Apps including revision management and event-driven autoscaling via KEDA; deploying and managing workloads on Azure Kubernetes Service (AKS) using manifest files; and monitoring/troubleshooting containerized solutions by inspecting logs, events, and end-to-end connectivity.
- 2.Develop AI solutions by using Azure data management services (25–30%): Covers connecting to and querying Azure Cosmos DB for NoSQL via SDK; optimizing query performance using indexing policies, consistency levels, and Request Units; storing and retrieving embeddings and executing vector similarity search for semantic retrieval; implementing change feed processors; connecting to and querying Azure Database for PostgreSQL via SDK; designing schemas and indexing strategies including pgvector optimization; running vector similarity search and implementing RAG patterns with metadata filtering; configuring compute/memory/storage for vector workloads; and integrating Azure Managed Redis for caching, expiration, invalidation, and vector indexing.
- 3.Connect to and consume Azure services (20–25%): Covers building event- and message-based AI solutions using Azure Service Bus (including dead-letter queue handling, topics, and subscriptions) and Azure Event Grid (including filters, custom events, and retries); building serverless APIs using Azure Functions with triggers and bindings; and configuring and deploying function apps.
- 4.Secure, monitor, and troubleshoot Azure solutions (20–25%): Covers securing secrets using Azure Key Vault including secret rotation and retrieval; storing and retrieving application configuration using Azure App Configuration; tracing distributed systems using OpenTelemetry SDKs; and writing KQL (Kusto Query Language) queries to analyze logs and metrics.
Study Tips
- Download and work through the official AI-200 Study Guide at https://learn.microsoft.com/credentials/certifications/resources/study-guides/ai-200 — it lists every assessable skill with sub-bullets that reveal the exact depth of knowledge required for each topic.
- Build hands-on labs around the four exam domains: deploy a containerized Python app to Azure Container Apps with KEDA scaling, store and query embeddings in both Cosmos DB for NoSQL and PostgreSQL with pgvector, then wire them together with Service Bus and monitor with OpenTelemetry.
- Practice writing KQL queries in the Azure Monitor Logs interface. The exam tests real query authoring, so use the Azure documentation's KQL tutorial and the Log Analytics demo environment to get comfortable filtering, aggregating, and joining log tables.
- Focus significant study time on vector database patterns — specifically how to implement RAG (Retrieval-Augmented Generation) using pgvector in Azure Database for PostgreSQL and the native vector search in Cosmos DB for NoSQL. These are new areas absent from AZ-204 and carry heavy exam weight within the 25–30% data management domain.
- Use the official exam sandbox (https://go.microsoft.com/fwlink/?linkid=2226877) to familiarize yourself with the interactive question types before exam day, as AI-200 may include drag-and-drop or lab-style components in addition to multiple-choice questions.
- Review the Azure SDKs for Python (azure-cosmos, psycopg2/asyncpg for PostgreSQL, azure-servicebus, azure-eventgrid, azure-keyvault-secrets, azure-functions) and practice writing code that uses each. The exam is code-oriented and expects you to identify correct SDK usage patterns.
- Study Azure Container Registry Tasks and AKS manifest-based deployments specifically — the exam distinguishes between App Service, Container Apps, and AKS deployment models and expects you to know when and how to use each, including KEDA-based autoscaling configuration for Container Apps.
Career Benefits
AI-200 positions developers at the intersection of cloud engineering and applied AI — one of the fastest-growing specializations in enterprise technology. As the designated successor to AZ-204, it signals to employers that a candidate can not only build cloud-native applications but can architect the back-end infrastructure that powers production AI systems, including vector retrieval pipelines, event-driven AI workflows, and observable, containerized deployments. Roles commonly pursued by AI-200 holders include Azure AI Developer, Cloud Solutions Engineer, AI Platform Engineer, and Backend Developer on AI product teams, with Azure developer salaries in the United States ranging from approximately $110,000 to $160,000 annually depending on seniority and location.
Beyond job titles, AI-200 also serves as a prerequisite for the Azure DevOps Engineer Expert certification, making it a strategic stepping stone for developers targeting senior or lead engineering credentials. As enterprises accelerate AI adoption on Azure, demand for developers who can build reliable, secure, and scalable AI backends — rather than just call AI APIs — is increasing sharply, giving this certification strong near-term market relevance.
Sample Questions
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 12 full-length practice exams.
Preview — answers shown1. Blue Yonder Airlines has deployed an AI-powered flight recommendation API as an Azure Container App. The security team rotated the Azure OpenAI API key stored in Azure Key Vault. The Container App references the secret using a Key Vault URL without a version identifier: `https://myvault.vault.azure.net/secrets/openai-api-key`. A developer wants to know when the updated key will take effect without any manual redeployment. What is the correct expected behavior? (Select one!)
AThe Container App automatically detects the secret change within approximately 30 minutes and restarts active revisions to apply the updated value.
BThe developer must create a new revision and redeploy the Container App before it will fetch the updated Key Vault secret.
CThe Container App resolves Key Vault secrets on every incoming HTTP request, so the updated key is used immediately for all subsequent API calls.
DThe Container App requires the managed identity to be re-granted the Key Vault Secrets User role after each secret rotation before it can retrieve the new value.
Explanation
Azure Container Apps using Key Vault references without a version identifier (latest-version references) automatically poll for secret changes approximately every 30 minutes. When an updated secret version is detected, active revisions are automatically restarted to pick up the new value, enabling zero-downtime secret rotation without manual redeployment. Key Vault secrets are loaded at container startup, not resolved per-request during runtime, so resolving on every HTTP request is incorrect. A new revision is only required when using a version-locked Key Vault reference where a specific version ID is appended to the URL; latest-version references auto-update without triggering a new revision. The managed identity role assignment only needs to be configured once and remains valid across secret rotations; re-granting the Key Vault Secrets User role is not part of the rotation process.
2. WingTip Toys is deploying a Python-based AI application that calls Azure OpenAI and Azure AI Search. The application uses DefaultAzureCredential from the azure-identity SDK for authentication. During local development, engineers authenticate using the Azure CLI. In Azure Container Apps staging and production environments, the application should authenticate using a system-assigned managed identity. A developer asks why no code changes are needed between environments. Which statement correctly explains how DefaultAzureCredential resolves the authentication source? (Select one!)
ADefaultAzureCredential attempts multiple credential providers in a defined order and automatically uses the first one that succeeds, including managed identity in Azure and Azure CLI on local workstations
BDefaultAzureCredential requires a static configuration file checked into the repository that maps environment names to their designated credential provider type
CDefaultAzureCredential reads an AZURE_CREDENTIAL_TYPE environment variable that specifies which credential provider to use in each deployment environment
DDefaultAzureCredential always uses environment variable credentials when any AZURE_-prefixed variable is present, falling back to managed identity only if all such variables are absent
Explanation
DefaultAzureCredential implements a credential chain that automatically evaluates multiple authentication providers in a defined sequence: environment variables, workload identity, managed identity, Azure CLI, Azure PowerShell, and interactive browser. In an Azure Container Apps environment with a configured system-assigned managed identity, the managed identity provider succeeds early in the chain and is used. On a local developer workstation where engineers are signed in with the Azure CLI, the Azure CLI provider succeeds later in the chain. Because the same DefaultAzureCredential() call is used in all environments and it selects the first successful provider automatically, no code changes or environment-specific branching logic are needed. There is no AZURE_CREDENTIAL_TYPE environment variable in the azure-identity SDK. DefaultAzureCredential does not shortcut its sequential evaluation based solely on the presence of AZURE_-prefixed environment variables. No static configuration file is required or referenced by the SDK.
3. Southridge Video runs an AI-powered video transcription pipeline that spans Azure Container Apps and Azure Functions. Support engineers need to trace individual transcription requests end-to-end across both services, identify latency bottlenecks at each processing stage, and correlate logs with specific request identifiers in Azure Monitor. What should the engineering team implement? (Select one!)
AInstrument both services with the OpenTelemetry SDK and configure an Azure Monitor exporter to send distributed traces and logs to Application Insights
BConfigure Azure Service Health alerts for the Container Apps and Functions resources to detect service degradation
CEnable Container Apps log streaming to capture stdout and stderr output from each container replica
DEnable Azure Diagnostics settings on each resource and write custom correlation IDs to Azure Storage diagnostic logs
Explanation
OpenTelemetry SDK instrumentation with an Azure Monitor exporter is the correct approach for distributed tracing across multiple Azure services. OpenTelemetry provides automatic context propagation that links spans across Container Apps and Azure Functions using trace and span IDs, enabling end-to-end trace visualization, per-stage latency breakdown, and request correlation within Application Insights. Diagnostic settings forward resource-level platform logs and metrics to storage or Log Analytics but do not perform distributed trace context propagation across service boundaries — custom correlation IDs in storage logs require additional tooling to correlate across services. Azure Service Health monitors Azure platform availability and planned maintenance events, not application-level request latency or processing pipeline performance. Container Apps log streaming captures real-time console output from individual containers but does not propagate trace context across services, correlate requests across Container Apps and Functions, or provide structured latency telemetry.
4. Fabrikam Healthcare is building a patient intake assistant that must perform clinical entity recognition on physician notes, analyze intake form images to extract structured fields, and transcribe spoken patient responses to text. The development team wants to authenticate all three capabilities using a single API key and route all requests through a single regional endpoint to simplify key rotation and consolidate billing. Which Azure AI resource type should they provision? (Select one!)
AAn Azure Machine Learning online endpoint with separate inference deployments for each capability behind a shared scoring URI
BA single multi-service Azure AI services resource that provides a unified endpoint and key covering Language, Vision, Speech, and other cognitive services
CAn Azure OpenAI resource with a GPT-4o deployment that natively handles all three modalities through a single multimodal API call
DThree separate single-service resources for Azure AI Language, Azure AI Vision, and Azure AI Speech, each with its own dedicated endpoint and API key
Explanation
The Azure AI services multi-service resource provides a single regional endpoint and a single API key that grants access to all included managed AI services, including Azure AI Language, Azure AI Vision, Azure AI Speech, and Azure AI Document Intelligence. This eliminates per-service key management and consolidates billing under one resource, satisfying all requirements. Provisioning three separate single-service resources requires managing three distinct endpoints and three API keys, introducing the operational complexity the team explicitly wants to avoid. An Azure OpenAI GPT-4o deployment handles multimodal inputs and generative tasks but does not expose the dedicated clinical entity recognition, structured form field extraction, or batch speech transcription APIs provided by the individual Azure AI services. Azure Machine Learning online endpoints are designed for deploying custom-trained models, not for consuming Microsoft-managed cognitive APIs behind unified authentication.
5. Blue Yonder Airlines is deploying an AI recommendation API to Azure Kubernetes Service. Application pods must retrieve model API keys and connection strings from Azure Key Vault at runtime. The security policy prohibits storing credentials in container images, Kubernetes Secrets objects, or shared node-level identities. Each pod deployment must have its own granular Azure identity scope. Which AKS identity configuration should the developer implement? (Select one!)
AAssign the AKS kubelet managed identity the Key Vault Secrets User role so all pods running on each node can access Key Vault through the shared node identity
BConfigure AKS workload identity by creating a Kubernetes service account, federating it with a user-assigned managed identity in Microsoft Entra ID, and assigning that identity the Key Vault Secrets User role
CUse the AKS control plane managed identity and assign it the Key Vault Secrets User role to allow all cluster workloads to share a single cluster-wide Azure identity
DCreate a Microsoft Entra service principal, assign it Key Vault Secrets User role, and reference the client secret using valueFrom.secretKeyRef in the pod specification
Explanation
AKS workload identity federates a Kubernetes service account token with a user-assigned managed identity in Microsoft Entra ID, enabling individual pods to authenticate to Azure services without credentials stored anywhere. Pod-level identity tokens are issued per service account, satisfying the requirement for granular per-deployment identity. The kubelet identity is a node-level identity shared by every pod running on the same node, violating the requirement for pod-granular identity scoping. A service principal with a client secret stored in a Kubernetes Secret object violates the policy prohibiting credential storage. The AKS control plane identity manages cluster infrastructure operations such as node pool provisioning and is not designed to authenticate individual application workloads.
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