Exam Name: Developing AI Cloud Solutions on Azure
Certification Provider: Microsoft
Corresponding Certification: Azure AI Engineer Associate
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- Total Questions: 144
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- Total Questions: 144
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Microsoft AI-200 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Microsoft Azure AI Solution |
| Exam Number: | AI-200 / AI-102 |
| Certificate Validity Period: | 1 year (renewable through Microsoft Learn assessment) |
| Passing Score: | 700/1000 |
| Exam Format: | Case studies, Scenario-based questions, Multiple choice, Multiple response |
| Exam Duration: | 100-120 |
| Exam Price: | USD 165 (varies by region) |
| Related Certifications: | Azure AI Fundamentals Azure Data Scientist Associate |
| Real Exam Qty: | 40-60 |
| Available Languages: | Chinese (Simplified), Korean, German, Japanese, English, Spanish, French |
| Recommended Training: | Microsoft Learn Azure AI Engineer Learning Path Azure OpenAI Service Documentation |
| Exam Registration: | Pearson VUE Microsoft Exams Microsoft Learn Certification Page |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam or onsite test center via Pearson VUE |
| Pre Condition: | No mandatory prerequisites, but recommended: Azure Fundamentals (AZ-900 or AI-900) and experience with Azure services and AI concepts. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-engineer/ |
Microsoft AI-200 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Select appropriate Azure AI services - Plan security and compliance requirements |
| Topic 2: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 3: Implement Azure AI solutions | - Implement computer vision solutions - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search |
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The Microsoft Developing AI Cloud Solutions on Azure exam contains 40-60 to complete within 100-120. Timed simulation in the ExamPrepAway Desktop Test Engine teaches that rhythm before exam day does.
Online proctored exam or onsite test center via Pearson VUE Register for the Microsoft Developing AI Cloud Solutions on Azure exam through these official channels:
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The Microsoft Developing AI Cloud Solutions on Azure exam requires 700/1000 to pass, and registration costs USD 165 (varies by region). A failed attempt means paying the same fee again — a readiness check with the ExamPrepAway engines before booking is the cheaper option.
The AI-200 exam is a Microsoft certification exam validating the Microsoft Developing AI Cloud Solutions on Azure syllabus shown above. It belongs to these credential paths: Azure AI Fundamentals, Azure Data Scientist Associate. Candidates in 2026 start with ExamPrepAway's 144 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
No mandatory prerequisites, but recommended: Azure Fundamentals (AZ-900 or AI-900) and experience with Azure services and AI concepts. Since requirements change over time, confirm the latest on the official Microsoft exam page before scheduling.
The official Microsoft Developing AI Cloud Solutions on Azure outline defines 3 domains. The leading three are Implement Azure AI solutions, Plan and manage Azure AI solutions, and Implement and monitor AI workloads. The full list is in the topics section above, and ExamPrepAway's 144 practice questions cover each one.
Microsoft recommends these training resources for the Microsoft Developing AI Cloud Solutions on Azure exam:
Follow the training with steady self-testing — the 144 practice questions from ExamPrepAway help you apply theory and fill any gaps the training leaves.
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 144 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
You plan to deploy a web application to AKS.
The solution must:
* Scale out the application by adding more pods during peak CPU usage
* Expose the application internally within the cluster only
You need to configure a Kubernetes resource for each requirement
Which resources should you configure? To answer, move the appropriate resources to the correct requirements You may use each resource once, more than once, or not at all. You may need to move split bar between .. or scroll to view content.
Correct Answer:
Explanation:
Verified Answer: Scale out the application: HorizontalPodAutoscaler. Expose internally only: ClusterIP service.
Detailed Explanation: The Kubernetes HorizontalPodAutoscaler increases or decreases pod replicas in response to CPU, memory, or other metrics, so it is the resource that implements CPU-driven pod scale-out. A ClusterIP service creates an internal service IP that is reachable within the Kubernetes cluster and is the normal choice for an internal-only service. A Deployment defines the workload but does not itself implement metric-driven autoscaling, while Ingress is intended to route inbound network traffic.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | AKS scaling overview | Kubernetes services in AKS
A RAG application ' s retrieval step is returning documents that are topically related but not precisely relevant to the user ' s question.
You need to improve retrieval precision without retraining any model.
What should you do?
- A. Enable semantic ranking (re-ranking) in Azure AI Search
- B. Increase the embedding model ' s dimensionality only
- C. Disable vector search and use only keyword search
- D. Reduce the number of indexed documents
Correct Answer: A 🗳️
Explanation: Only visible for ExamPrepAway members. You can sign-up / login (it's free).
You are developing a microservices-based application that uses Azure Container Apps The application consists of several containerized services that handle tasks, such as processing orders, managing inventory, and generating reports.
You must secure the container apps. All apps must reside in the same virtual network, share the same Dapr configuration, and share the same logging location.
Apps must support the configuration of the amount of memory and compute resources available to containers.
You need to configure the Azure Container App
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Verified Answer: `az containerapp env create ... --enable-workload-profiles`.
Detailed Explanation: The shared boundary for Container Apps is the managed environment. Apps in one environment can share the environment's virtual network integration, observability destination, and Dapr- related environment configuration. Enabling workload profiles provides selectable compute profiles and resource sizing options for workloads in that environment. The CLI must therefore create a Container Apps environment and enable workload profiles. Creating an individual container app alone would not establish the common environment-level network, logging, and resource-profile boundary required by the scenario.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Azure Container Apps environments
An AI platform uses App Configuration for feature flags and endpoint routing.
The platform stores secrets alongside configuration data and does NOT support dynamic configuration refresh.
You need to redesign the configuration approach so that:
configuration supports dynamic refresh;
secrets are stored outside App Configuration;
the application can securely access both App Configuration and Key Vault.
You need to enable secure dynamic configuration management for the platform.
Which three actions should you perform? Each correct answer presents part of the solution. Choose three.
NOTE: Each correct selection is worth one point.
- A. Use managed identity for both App Configuration and Key Vault access.
- B. Store API keys in Key Vault.
- C. Use a service principal secret for both App Configuration and Key Vault access.
- D. Store configuration values in environment variables.
- E. Allow configuration updates with a polling interval.
Correct Answer: A,B,E 🗳️
Explanation: Only visible for ExamPrepAway members. You can sign-up / login (it's free).
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Viewing page 1 out of 29 pages.
Viewing questions 1-5 out of 144 questions.
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