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. Litware Analytics is building a nightly batch embedding pipeline using Azure Functions. The function must generate embeddings for newly ingested documents and write results to Azure Cosmos DB. The job must execute at exactly 2:00 AM UTC every day. Which NCRONTAB expression should the developer configure for the Azure Functions Timer trigger? (Select one!)
A0 2 * * *
B* 2 0 * * *
C0 2 0 * * *
D0 0 2 * * *
Explanation
Azure Functions Timer triggers use a 6-field NCRONTAB expression with the structure: {second} {minute} {hour} {day-of-month} {month} {day-of-week}. To trigger at exactly 2:00:00 AM UTC every day, the correct expression is 0 0 2 * * * — second=0, minute=0, hour=2, with wildcards for day-of-month, month, and day-of-week. The 5-field cron format that omits seconds is standard in Unix cron tools but is not valid for Azure Functions and will fail to parse at runtime. The expression * 2 0 * * * fires every second during minute 2 of hour 0, which means the function would invoke 60 times at 12:02 AM rather than once at 2:00 AM. The expression 0 2 0 * * * fires at second=0, minute=2, hour=0, which resolves to 12:02:00 AM — not 2:00 AM. Developers frequently confuse 5-field and 6-field NCRONTAB formats when migrating scheduled jobs from standard Unix cron systems or cloud schedulers to Azure Functions.
2. Adatum AI is building a semantic search feature on Azure Database for PostgreSQL Flexible Server using the pgvector extension. Their document embedding vectors are not L2-normalized, and the team requires retrieval ranked by cosine similarity to correctly measure directional similarity regardless of vector magnitude. Which SQL operator should they use in the ORDER BY clause of their similarity query? (Select one!)
A<->
B<+>
C<=>
D<#>
Explanation
The <=> operator in pgvector computes cosine distance between two vectors. For unnormalized vectors, cosine distance is the correct metric because it measures directional similarity by normalizing magnitudes internally, producing rankings based purely on angular difference. The <-> operator computes Euclidean (L2) distance, which measures absolute geometric distance between vectors and produces different rankings than cosine for unnormalized data. The <#> operator computes the negative inner product, which is equivalent to cosine similarity only for already-normalized vectors — using it on unnormalized vectors yields incorrect semantic rankings. There is no <+> operator in the pgvector extension.
3. Adatum Research stores 750,000 product description embeddings, each with 1,536 dimensions, in Azure Cosmos DB for NoSQL. The team needs to configure a vector index that delivers the lowest query latency and highest throughput for approximate nearest neighbor search. A minor reduction in recall accuracy is acceptable. Which Cosmos DB for NoSQL vector indexing algorithm should they configure? (Select one!)
AdiskANN
Bhnsw
CquantizedFlat
Dflat
Explanation
diskANN is Microsoft Research's graph-based approximate nearest neighbor algorithm integrated into Cosmos DB for NoSQL. It is optimized for datasets exceeding 50,000 vectors and delivers the best combination of query latency and throughput at scale. It supports up to 4,096 dimensions, making it fully compatible with 1,536-dimensional embeddings. flat performs brute-force exact search and is hard-limited to a maximum of 505 dimensions, making it entirely incompatible with 1,536-dimensional vectors regardless of dataset size. quantizedFlat applies DiskANN-based compression but is designed for datasets below 50,000 vectors and does not deliver optimal latency or throughput at 750,000 vectors. hnsw is a valid vector indexing algorithm in pgvector on Azure Database for PostgreSQL and in Azure Managed Redis, but it is not a supported indexing algorithm in Cosmos DB for NoSQL.
4. Northgate AI is building a product catalog search system backed by Azure Cosmos DB for NoSQL. The catalog currently contains 200,000 product embeddings, each with 1,536 dimensions. The team requires the highest query throughput and lowest latency for approximate nearest neighbor search with no budget constraint. Which vector indexing algorithm should they configure in the container's vector policy? (Select one!)
Aflat
BdiskANN
CquantizedFlat
DHNSW
Explanation
diskANN is Microsoft's proprietary graph-based algorithm integrated into Cosmos DB for NoSQL, specifically optimized for datasets larger than 50,000 vectors. It provides the best combination of query latency and throughput for approximate nearest neighbor search and supports up to 4,096 dimensions, well within the 1,536-dimension requirement. With 200,000 embeddings, diskANN delivers superior performance compared to alternatives. The flat algorithm performs brute-force exact search with 100% recall accuracy but is limited to a maximum of 505 dimensions, making it incompatible with 1,536-dimensional vectors. quantizedFlat uses DiskANN-based compression and is optimal for datasets under 50,000 vectors, making it suboptimal at the 200,000-vector scale. HNSW is a PostgreSQL pgvector indexing algorithm and is not a valid vector indexing option available in Cosmos DB for NoSQL.
5. Wide World Importers is deploying an AI image classification service to Azure Kubernetes Service. Container images are stored in Azure Container Registry. A security audit requires that the AKS cluster must pull images without enabling the ACR admin account. The development team must assign the minimum required Azure RBAC role to the AKS agent pool identity on the ACR resource. Which role should be assigned? (Select one!)
AAcrPush
BAcrPull
CContributor
DReader
Explanation
AcrPull is the purpose-built role that grants the AKS agent pool's Kubelet identity the minimum permission required to pull container images from Azure Container Registry. This follows the principle of least privilege and avoids the security risk of enabling the ACR admin account, which uses static long-lived credentials. Contributor grants broad management permissions far exceeding what image pulls require and violates least privilege. AcrPush adds write access to push new images to the registry, which is unnecessary and excessive for deployment nodes that only need to pull. Reader grants permission to view Azure resource properties but does not include the data plane operations needed to pull container image layers from the registry.
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