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. Lucerne Publishing is running a containerized AI content recommendation service on Azure Container Apps. The platform team wants to deploy a new version of the inference container with only 10% of live traffic routed to it while validating performance, keeping 90% on the current version. The Container App currently uses the default revision mode. What must the platform team configure before traffic splitting between revisions is possible? (Select one!)
AAssign a unique DNS hostname to each container revision
BEnable multiple revision mode on the Container App
CCreate a dedicated Container App environment for the new revision
DConfigure a KEDA scale rule with a weighted traffic percentage trigger
Explanation
Multiple revision mode must be enabled before traffic splitting between revisions is possible. In the default single revision mode, each new deployment replaces the previous active revision, so only one revision can receive traffic at a time. Enabling multiple revision mode allows simultaneous active revisions with configurable traffic weights, directly supporting canary deployment patterns such as routing 10% to a new version. Creating a separate Container App environment is unnecessary and isolates the new version entirely from live traffic rather than splitting it. KEDA scale rules govern autoscaling based on event sources such as queue depth or Service Bus message counts and have no role in distributing traffic percentages between revisions. Each revision already receives its own ingress URL by default; assigning additional hostnames does not enable weighted traffic splitting.
2. Lamna Healthcare is building a patient case similarity search system on Azure Database for PostgreSQL Flexible Server. The system stores 75,000 medical case embeddings in a vector column and is growing rapidly. Query latency has become unacceptable under the current configuration. The team requires recall accuracy above 99 percent and fast query performance at this scale, and can accept a longer one-time index build period. Which vector index type should the team create? (Select one!)
AAn IVFFlat index with a lists parameter of 100 for fast approximate queries
BA B-tree index on the embedding column to support efficient range filtering
CAn HNSW index with default m and ef_construction parameters
DNo index, relying on sequential scan for guaranteed 100 percent recall accuracy
Explanation
HNSW (Hierarchical Navigable Small World) is the appropriate vector index for datasets larger than 50,000 vectors when the requirements are high recall accuracy and fast query performance. HNSW achieves approximately 99 percent recall and provides the fastest query times among the available index types, at the cost of a slower initial build and higher memory consumption. Both trade-offs align with the stated requirements. Sequential scan provides exact results but scales linearly with record count, making it impractical beyond a few thousand vectors. IVFFlat is better suited for datasets below 50,000 vectors and achieves approximately 95 percent recall, which does not satisfy the greater-than-99-percent accuracy requirement. B-tree indexes operate on scalar values and have no capability to compute or accelerate vector distance operations.
3. Wingtip Toys has enabled diagnostic logging on Azure Key Vault to audit all certificate management operations performed by their automated CI/CD deployment pipeline. The logs are being sent to an Azure Storage account. A security engineer needs to write a KQL query to confirm that TLS certificates were successfully imported into Key Vault during the latest deployment run. Which log container name and operation name should the engineer reference in the query? (Select one!)
AContainer `insights-logs-auditevent`, operation name `CertificateImport`
BContainer `AzureDiagnostics`, operation name `CertificateUpload`
CContainer `insights-logs-operationallog`, operation name `CertificateSet`
DContainer `AzureActivity`, operation name `CertificateCreate`
Explanation
Azure Key Vault diagnostic logs are stored in the `insights-logs-auditevent` container within the configured storage account or Log Analytics workspace. The operation name `CertificateImport` is the specific audit event recorded when a certificate is programmatically imported into Key Vault, which directly matches the CI/CD pipeline scenario. Key Vault audit logs can have up to 10 minutes of latency before appearing in the destination. The `AzureActivity` table captures Azure Resource Manager control plane events such as creating or deleting the vault itself and does not record data plane operations like certificate imports. The container name `insights-logs-operationallog` does not exist for Key Vault diagnostics. `CertificateCreate`, `CertificateUpload`, and `CertificateSet` are not valid Key Vault operation names.
4. Tailwind Traders is building a customer support ticket routing system. The system must detect the intent behind customer messages (for example, 'track my order' versus 'request refund' versus 'report damaged item') and also extract business-specific entities such as order numbers and product SKUs. Standard pre-built entity categories do not cover the company's vocabulary. Which Azure AI Language feature should the team use? (Select one!)
AAzure AI Content Safety text analysis with custom category blocklists
BAzure AI Translator with language detection and custom glossary alignment
CConversational Language Understanding with custom intent classification and entity extraction
DPre-built Named Entity Recognition with standard category detection
Explanation
Conversational Language Understanding (CLU) is the Azure AI Language feature designed for building custom intent classification and entity extraction models. CLU supports labeling and training on business-specific intents and named entities, making it appropriate for ticket routing scenarios where the vocabulary and categories are unique to the organization. Pre-built Named Entity Recognition recognizes general categories such as dates, locations, and persons but cannot be trained on custom business intents like 'track my order.' Azure AI Translator handles cross-language translation and does not perform intent classification or entity labeling. Azure AI Content Safety is designed to detect harmful or unsafe content categories and is not capable of classifying customer intent or extracting domain-specific business entities.
5. Tailspin Toys is deploying an AI image processing service to Azure Container Apps. The service reads job messages from an Azure Service Bus queue and performs inference on each image. During peak promotional periods, the queue can accumulate thousands of messages. The team requires replicas to scale automatically based on queue depth and scale to zero during idle periods to minimize costs. Which scaling configuration should they implement? (Select one!)
AConfigure a KEDA Azure Service Bus scaling rule specifying the queue name and message count threshold; set minReplicas to 0 and maxReplicas to 20
BConfigure an HTTP scaling rule with a concurrent request threshold of 100; set minReplicas to 0 and maxReplicas to 20
CEnable Azure Monitor autoscale on the Container Apps environment with a custom metric sourced from Service Bus queue length
DConfigure a KEDA CPU-based scaling rule targeting 70 percent CPU utilization; set minReplicas to 0 and reference the Service Bus queue as the CPU source
Explanation
KEDA (Kubernetes Event-Driven Autoscaling) is natively integrated into Azure Container Apps and supports event-driven scaling rules driven by external message sources including Azure Service Bus. The Service Bus KEDA scaler monitors queue message count and adjusts replica count proportionally. Setting minReplicas to 0 enables scale-to-zero behavior, eliminating compute cost when the queue is idle. HTTP scaling rules respond to incoming HTTP request concurrency and cannot observe Service Bus queue depth, making them unsuitable for queue-driven workloads. CPU-based scaling rules measure resource utilization on running replicas, not external event queue depth, so they cannot drive scale-to-zero based on an empty Service Bus queue. Azure Monitor autoscale is the scaling mechanism for App Service plans and Virtual Machine Scale Sets; Container Apps uses KEDA-based scaling rules configured at the application level.
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