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. Northwind Traders receives thousands of vendor invoices daily in PDF and image formats. Each invoice shares a consistent layout with fields including vendor name, invoice number, line items, and total amount. A developer must build a solution that extracts structured data from these documents with minimal training data preparation. Which Azure AI service and model type is most appropriate for this requirement? (Select one!)
AAzure AI Document Intelligence's prebuilt-invoice model, which is pre-trained to extract standard invoice fields including vendor name, invoice number, line items, and totals without requiring custom training data
BAzure AI Language's custom Named Entity Recognition model trained on labeled invoice samples to extract domain-specific entity fields from extracted text
CAzure AI Vision's optical character recognition read model to extract raw text, followed by a custom parsing script using regular expressions to identify fields
DAzure AI Document Intelligence's custom extraction model, which requires a set of labeled document samples to learn domain-specific field locations before deployment
Explanation
Azure AI Document Intelligence's prebuilt-invoice model is purpose-built to recognize and extract standard invoice fields such as vendor name, invoice ID, purchase order number, invoice date, line items, subtotals, tax amounts, and totals directly from PDFs and images with no custom training or labeled data required. Using Azure AI Vision OCR returns raw unstructured text without field-level semantic understanding, requiring complex custom parsing logic that must account for layout variation. Azure AI Language Custom NER extracts named entities from plain text but lacks the layout-aware document understanding needed for structured extraction from PDFs and images, and requires significant labeled training data. Custom extraction models in Document Intelligence can be trained with as few as 5 labeled samples and would add unnecessary complexity when the prebuilt invoice model already addresses the use case without any training overhead.
2. Bellows College is building a student-facing AI tutoring chatbot powered by Azure OpenAI. The platform serves students aged 13–18 and must meet educational content guidelines requiring: blocking all hate speech at severity level 2 or above; blocking any self-harm content at severity level 1 or above; and allowing general historical violence discussions at severity level 2 but blocking level 3 and above. Which service and configuration meets all three requirements? (Select one!)
AAzure AI Content Moderator with custom word lists and category-level blocking enabled for hate speech and violence
BAzure AI Content Safety with per-category severity thresholds configured independently for hate speech, self-harm, and violence
CAzure AI Content Safety with a single global severity threshold of 1 applied uniformly across all harm categories
DAzure OpenAI built-in content filtering using default moderate sensitivity settings with no additional threshold customization
Explanation
Azure AI Content Safety supports independent per-category severity threshold configuration, allowing hate speech, self-harm, violence, and sexual content to each have a different blocking threshold. Setting hate speech to block at severity 2 or above, self-harm to block at severity 1 or above, and violence to block at severity 3 or above exactly matches all three requirements — permitting level-2 historical violence while protecting students from higher-severity content and stricter categories. Azure AI Content Moderator is the legacy service superseded by Azure AI Content Safety and does not offer the granular per-category numeric severity threshold model required for this use case. A single global threshold of 1 applied uniformly would incorrectly block historical violence content at severity level 2, violating the explicit requirement that level-2 violence is permitted. Azure OpenAI built-in content filtering provides fixed default sensitivity levels and does not expose the per-category severity threshold customization needed to satisfy the specific educational compliance requirements described.
3. Northwind AI has deployed its language model inference API as an Azure Container App. A new container image containing an updated model version is ready for production validation. The team wants to route 10% of live traffic to the new revision while keeping 90% on the stable revision. If latency and accuracy metrics confirm acceptable performance, they will gradually increase the new revision's share. Which configuration change is required to enable this strategy? (Select one!)
ASwitch the Container App to multiple revision mode and configure traffic weight percentages across the two simultaneously active revisions
BCreate two separate Container Apps environments and use Azure Application Gateway with weighted backend pools to distribute requests
CDeploy a second Container App with the new image and configure Azure Traffic Manager to perform weighted DNS-based routing between the two apps
DKeep the Container App in single revision mode and specify traffic weight values for each revision in the deployment manifest
Explanation
Multiple revision mode in Azure Container Apps is the purpose-built mechanism for this scenario. When enabled, multiple revisions can be simultaneously active and receive traffic according to configurable weight percentages, enabling canary deployments and gradual rollouts of new model versions. Single revision mode permits only one active revision at a time — deploying a new image immediately replaces the previous revision and provides no traffic splitting capability. Azure Traffic Manager performs DNS-based routing between separate Azure resources, not between revisions of a single Container App, and introduces unnecessary infrastructure complexity. Using separate Container Apps environments with Application Gateway is an over-engineered approach that incurs additional cost and management overhead when the built-in revision traffic-splitting feature directly addresses the requirement.
4. Blue Yonder Airlines has deployed a multi-service flight search platform on Azure consisting of several Azure Container Apps microservices that call Azure OpenAI, Azure AI Search, and Azure Cosmos DB. The operations team observes intermittently high end-to-end response times but cannot identify which downstream service in the call chain is responsible for the latency spike. The team needs a solution that correlates individual requests across all service boundaries and shows the precise latency contribution of each downstream call within a single unified view. Which approach should the developer implement? (Select one!)
AEnable Azure Container Apps built-in HTTP request logging and analyze p95 latency percentiles per microservice in isolation
BInstrument each microservice with the OpenTelemetry SDK and export distributed traces to Azure Monitor using the Azure Monitor OpenTelemetry Exporter
CEnable Azure Monitor diagnostic settings on each Azure service and write KQL queries to compare average response-time metrics across services
DConfigure Application Insights connection strings in each microservice and rely on its automatic dependency tracking for cross-service call correlation
Explanation
Instrumenting each microservice with the OpenTelemetry SDK and exporting traces to Azure Monitor via the Azure Monitor OpenTelemetry Exporter enables end-to-end distributed tracing across all service boundaries. OpenTelemetry propagates a trace context through HTTP and gRPC headers at every service-to-service call, producing a unified trace that shows each individual span — including calls to Azure OpenAI, Azure AI Search, and Azure Cosmos DB — with its precise latency contribution. Azure Monitor ingests OpenTelemetry trace data natively through the exporter, requiring no proprietary SDK changes. Enabling diagnostic settings and querying aggregate metrics with KQL surfaces resource-level averages and event logs but does not correlate the path of an individual request across multiple service boundaries into a single trace. Azure Container Apps HTTP logging captures only the entry-point response time for each microservice independently and has no visibility into the time spent waiting on downstream service calls within that microservice. Application Insights automatic dependency tracking is an older proprietary instrumentation approach that provides limited control over trace context propagation and lacks the cross-language, vendor-neutral flexibility needed to consistently correlate calls across heterogeneous microservices calling multiple AI backends.
5. Contoso AI Security is configuring Azure Monitor diagnostic settings for an Azure Key Vault instance that stores AI model API keys and connection strings. The compliance team must capture all vault operations for a quarterly security audit and needs to understand the correct log destination container name and the expected maximum log delivery delay. Which two statements accurately describe Azure Key Vault diagnostic logging? (Select two!)
Multiple correct answersAKey Vault audit and operational events are written to the insights-logs-auditevent diagnostic container
BKey Vault audit and operational events are written to the insights-logs-operationallogs diagnostic container
CThe maximum expected log delivery latency for Key Vault operations is 10 minutes
DThe maximum expected log delivery latency for Key Vault operations is 30 seconds
EKey Vault diagnostic logging only captures certificate operations; secret and key operations require Microsoft Defender for Key Vault to be enabled
Explanation
Azure Key Vault routes all diagnostic audit events to the insights-logs-auditevent container when diagnostic settings are configured to send logs to a storage account or Log Analytics workspace. Logged operations include VaultGet, SecretSet, KeyCreate, and CertificateImport, covering all secret, key, and certificate operations by default. The maximum expected latency before Key Vault operations appear in the diagnostic logs is 10 minutes, meaning security queries and alert rules should account for this delay when building near-real-time monitoring. insights-logs-operationallogs is not a valid Key Vault diagnostic container name. A 30-second latency does not accurately reflect Key Vault log delivery behavior. Microsoft Defender for Key Vault provides advanced threat detection capabilities such as anomaly alerting, but enabling it is not required to capture basic operational events in diagnostic logs.
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