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Developing AI Cloud Solutions on Azure: AI-200 Exam
"Developing AI Cloud Solutions on Azure", also known as AI-200 exam, is a Microsoft Certification. With the complete collection of questions and answers, BraindumpsPass has assembled to take you through 144 Q&As to your AI-200 Exam preparation. In the AI-200 exam resources, you will cover every field and category in Azure AI Engineer Associate Certification helping to ready you for your successful Microsoft Certification.
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- Updated on: Sep 28, 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 computer vision solutions - Implement natural language processing solutions - Implement knowledge mining with Azure AI Search - Implement generative AI solutions using Azure OpenAI |
| Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Select appropriate Azure AI services - Plan security and compliance requirements |
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
A python web API uses OpenTelemetry for tracing. The call downstream service function makes an outbound HTTP request by using the requests library. The following code is the only OpenTelemetry configuration in the application:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selectin is worth one point.
Correct Answer:
Explanation:
You are developing an Azure Functions app.
All functions in the app meet the following requirements:
* Run until either a successful run or until 10 run attempts occur.
* Ensure that there are at least 20 seconds between attempts for up to 15 minutes.
You need to configure the host.json file.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
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. Enable HTTP ingress concurrency scaling
- B. Increase the scaling rule to allow for the maximum running replica count.
- C. Configure the scaling rule to allow for the termination of all active replicas.
- D. Configure a Kubernetes Event-driven Autoscaler rule that monitors queue length.
Correct Answer: C,D 🗳️
Explanation: Only visible for BraindumpsPass members. You can sign-up / login (it's free).
You deploy an AI application across multiple Azure regions.
The application must be able to view writes across all regions within a predictable time window.
You need to determine the appropriate consistency level.
What are two consistency levels you can use to achieve the goal? Each correct answer presents a complete solution. Choose two.
NOTE: Each correct selection is worth one point.
- A. Strong
- B. Eventual
- C. Session
- D. Bounded staleness
Correct Answer: A,D 🗳️
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.
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.
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