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Updated: Sep 12, 2026
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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 |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | 1 year (renewable through Microsoft Learn assessment) |
| Exam Price: | USD 165 (varies by region) |
| Related Certifications: | Azure AI Fundamentals Azure Data Scientist Associate |
| Exam Format: | Case studies, Multiple choice, Scenario-based questions, Multiple response |
| Exam Duration: | 100-120 |
| Available Languages: | English, Chinese (Simplified), Korean, German, French, Spanish, Japanese |
| Real Exam Qty: | 40-60 |
| Recommended Training: | Azure OpenAI Service Documentation Microsoft Learn Azure AI Engineer Learning Path |
| Exam Registration: | Microsoft Learn Certification Page Pearson VUE Microsoft Exams |
| Sample Questions: | Microsoft AI-200 Sample Questions |
| 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 |
|---|---|
| Implement Azure AI solutions | - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI - Implement computer vision solutions |
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
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
You provisioned an Azure Cosmos DB for NoSQL account named account1 with the default consistency level.
You plan to configure the consistency level on a per request basis. You plan to request Consistent Prefix consistency on a per-request basis.
You need to identify the resulting consistency level for read and write operations.
Which levels result from this configuration? To answer, select the appropriate options in the answer a rea.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Verified Answer: Read operations: Consistent Prefix. Write operations: Session (the account default remains effective for writes).
Detailed Explanation: Azure Cosmos DB allows a client or request to override consistency for reads.
Microsoft explicitly notes that such an override applies only to reads; it does not change how writes are committed and replicated under the account's configured consistency. Because a new account uses Session consistency by default, requesting Consistent Prefix affects the reads while the account continues to use its Session consistency behavior for writes. This distinction is the key point tested by the hotspot.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Manage Cosmos DB consistency levels | Cosmos DB consistency level choices
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column. The schema must support vector similarity search and reliable date filtering You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
- A. Store embeddings in a varchar column.
- B. Store created_at as a free-form string.
- C. Use a typed timestamp column for created_at.
- D. Store embeddings in a pgvector column.
Correct Answer: C,D 🗳️
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You deploy a production Azure Function app that connects to an Azure SQL Database.
The solution must provide the following functionality:
* Prevent secrets from being exposed in source control.
* Support secret rotation without redeploying the function app.
* Avoid downtime during credential updates.
You need to configure secure and maintainable secret management. What should you configure?
- A. Hard coded connection string in the startup class
- B. Parameter file stored in source control
- C. Application settings with Key Vault references
- D. Environment variables in local.settings.json
Correct Answer: C 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You are developing a Retrieval-Augmented Generation (RAG) solution for a company. AI responses and embedding vectors are cached in Redis. The solution must meet the following requirements.
* AI responses must expire exactly 24 hours after they are cached
* Cached embeddings must always reflect the current source data.
You need to configure Redis to meet the requirements.
Correct Answer:
Explanation:
I response expiry: set a 24-hour TTL on each key. Embedding freshness: delete related cache keys when the source document changes.
Detailed Explanation: A fixed TTL encodes the requirement that an AI response must expire after a defined period rather than survive indefinitely or have its lifetime extended on every read. Embedding freshness is a separate consistency requirement: when the authoritative document changes, any cached representation derived from the old document must be invalidated immediately. Memory eviction policies are capacity- management mechanisms and cannot guarantee a precise 24-hour lifetime or source-data consistency.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Azure Managed Redis documentation
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