Microsoft AI-200 : Developing AI Cloud Solutions on Azure

AI-200 real exams

Exam Code: AI-200

Exam Name: Developing AI Cloud Solutions on Azure

Updated: Sep 12, 2026

Q & A: 144 Questions and Answers

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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Implement and monitor AI workloads- Monitor performance and troubleshoot issues
- Deploy AI models and services
Implement Azure AI solutions- Implement natural language processing solutions
- Implement generative AI solutions using Azure OpenAI
- Implement knowledge mining with Azure AI Search
- Implement computer vision solutions
Plan and manage Azure AI solutions- Select appropriate Azure AI services
- Monitor and optimize AI solutions
- Plan security and compliance requirements

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question #1

A company uses Azure Monitor Application Insights to monitor application behavior, including incoming requests and dependencies.
You must identify failed requests from the last hour. You must also calculate the average duration of failed request dependency calls, grouped by operation name.
You need to analyze telemetry in Application insights.
Which operators should you mm? To answer, select the appropriate options in the answer area NOTE: Each cot reel selection is worth one point.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:

Question #2

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: Embed the API key as a hardcoded environment variable in the Dockerfile.
Does the solution meet the goal?

  • A. No
  • B. Yes
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #3

You need to implement the semantic retrieval workflow for the recommendation engine to meet the technical and performance requirements of Fabrikam Inc.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Reveal Solution  Discussion  0

Correct Answer:


Explanation:
* Define a table schema with vector and metadata columns.
* Load embedding vectors and associated product metadata.
* Configure a Hierarchical Navigable Small World (HNSW) index on the embedding vector columns.
* Perform a similarity search using a WHERE clause and the < = > operator.
The workflow must first establish a PostgreSQL schema containing both the pgvector embedding column and the product metadata required for filtering. The embeddings and their associated metadata are then bulk- loaded. This ordering is important because Microsoft recommends loading data before creating vector indexes ; creating the index afterward provides faster ingestion and a more optimal index layout.
After loading, configure an HNSW index on the embedding column. HNSW is an approximate-nearest- neighbor index supported by pgvector and provides a strong speed/recall tradeoff for low-latency vector retrieval. Microsoft specifically documents HNSW for efficient cosine-distance searches in Azure Database for PostgreSQL.
Finally, execute the retrieval query so that mandatory product metadata constraints are applied through a WHERE clause , while vector similarity is evaluated with the < = > cosine-distance operator . This satisfies Fabrikam ' s requirement to calculate similarity only for eligible products.
A B-tree index is not an ANN vector index, and increasing Redis memory does not implement PostgreSQL semantic retrieval.
Study Guide references: Azure Database for PostgreSQL # pgvector, HNSW indexing, bulk-load optimization, vector similarity operators, metadata-filtered retrieval.

Question #4

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.

Reveal Solution  Discussion  0

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

Question #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. strong consistency
  • B. indexing precision
  • C. autoscale throughput
  • D. lease container
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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