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Last Updated: Oct 03, 2026
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Microsoft AI-200 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement natural language processing solutions - Implement computer vision solutions - Implement knowledge mining with Azure AI Search |
| 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 are deploying a model in Azure AI Foundry and must ensure the endpoint can handle unpredictable bursts of traffic while keeping cost low during idle periods.
Which deployment type should you choose?
- A. Provisioned Throughput Units (PTU)
- B. Dedicated managed compute with fixed capacity
- C. Batch deployment
- D. Standard (pay-as-you-go) deployment
Correct Answer: D 🗳️
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You need to configure the database resources for the Azure Database for PostgreSQL instance.
How should you complete the configuration to meet the business and technical requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* Meet the 200-ms semantic search latency requirement: Increase compute vCores.
* Optimize the environment for high-dimensional pgvector index residency: Increase memory allocation.
* Support the continuous ingestion of transaction-based embeddings: Enable storage autoscale.
For the strict sub-200-ms vector-search latency target, increasing compute vCores is the appropriate choice.
Vector similarity operations are computationally intensive, and additional CPU capacity improves mathematical throughput and parallel query execution. Microsoft's pgvector guidance emphasizes query-plan optimization, ANN indexes such as HNSW, and sufficient compute resources when optimizing vector workloads.
For high-dimensional pgvector index residency , increase memory allocation . HNSW provides strong query-performance characteristics but consumes more memory than IVFFlat. Keeping frequently accessed vector index structures in memory minimizes disk access and materially improves latency. Microsoft explicitly notes that HNSW requires more memory while providing a better speed/recall tradeoff.
For the continuous ingestion of millions of embeddings, enable storage autoscale . Azure Database for PostgreSQL Flexible Server can automatically increase allocated storage as capacity approaches configured thresholds, avoiding an out-of-storage condition as data volumes grow. Microsoft recommends storage autogrow for workloads whose storage demand can increase dynamically.
Increasing max_connections does not directly improve vector computation or index residency, read replicas primarily scale reads, and backup retention does not address ingestion capacity.
Study Guide references: Azure Database for PostgreSQL Flexible Server # pgvector performance optimization; compute and memory sizing; HNSW indexing; storage autogrow.
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 customer-managed key (CMK) and store the key in a new Azure Key Vault instance.
- 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 new container to include an encryption policy with the JSON properties to be encrypted.
Correct Answer: A 🗳️
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You plan to deploy a container to an Azure App Service API app named api1. You host the source code for api1 in a GitHub repository. The container uses the API key at runtime to connect to a backend service.
The container must be able to retrieve the API key at runtime without exposing it in the source repository or Git commit history. The API key must not be stored in application configuration directly. The API key must be accessed securely from Azure Key Vault.
You need to ensure that the API key is stored securely in Azure Key Vault and is available to the container at runtime without being exposed in source control or Git commit history.
Solution: Store the API key as an App Service application setting configured through the Azure Portal.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
A customer support bot built with Azure AI Foundry Agent Service must ground its answers strictly in an internal knowledge base and avoid hallucinating unsupported facts.
What should you implement?
- A. Use a larger context window model with no retrieval
- B. Connect the agent to an Azure AI Search knowledge source and instruct it to answer only from retrieved content
- C. Increase temperature to encourage exploration
- D. Disable content filtering to allow more flexible answers
Correct Answer: B 🗳️
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