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Microsoft AI-200 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Secure, monitor, and optimize AI solutions | 20% | - Manage security and configuration
|
| Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
1. Your chat application calls Azure OpenAI Service. You need to prevent the model from returning content that promotes self-harm, even if the prompt is crafted to bypass instructions. What should you configure?
A) Azure AI Content Safety filters on the Azure OpenAI resource
B) A lower max_tokens value
C) Azure AI Search semantic ranking
D) A system message instructing the model to refuse harmful topics
2. You are developing a Java application to be deployed in Azure. The application stores sensitive data in Azure Cosmos DB.
You need to configure Always Encrypted to encrypt the sensitive data inside the application.
What should you do first?
A) Create a new container to include an encryption policy with the JSON properties to be encrypted.
B) Create a data encryption key (DEK) by using the Azure Cosmos DB SDK and store the key in Azure Cosmos DB.
C) Create a Microsoft Entra ID managed identity and assign the identity to a new Azure Key Vault instance.
D) Create a customer-managed key (CMK) and store the key in a new Azure Key Vault instance.
3. You are evaluating a fine-tuned Azure OpenAI model against the base model before promoting it to production. Which Azure AI Foundry capability should you use?
A) Azure AI Search indexer scheduling
B) Azure Monitor Application Insights only
C) Content Safety category configuration
D) Prompt flow bulk testing / evaluation with metrics (groundedness, coherence, relevance)
4. A development team wants to compare multiple prompt variations against the same test dataset and visualize which prompt performs best. What should they use in Azure AI Foundry?
A) Speech Studio custom voice
B) Model deployment quota management
C) Azure AI Search indexers
D) Prompt flow with variants
5. You deploy multiple instances of a change feed processor to handle a high ingestion rate within Azure Cosmos DB for NoSQL.
Each processor instance must process a different subset of partitions.
You need to ensure the workload is load-balanced across all processor instances.
What should you configure?
A) indexing precision
B) lease container
C) autoscale throughput
D) strong consistency
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |


