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Microsoft AI-200 Exam Braindumps - in .pdf Free Demo

  • Exam Code: AI-200
  • Exam Name: Developing AI Cloud Solutions on Azure
  • , Last Updated: Aug 09, 2026
  • Q & A: 93 Questions and Answers
  • Convenient, easy to study. Printable Microsoft AI-200 PDF Format. It is an electronic file format regardless of the operating system platform. 100% Money Back Guarantee.
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  • Exam Code: AI-200
  • Exam Name: Developing AI Cloud Solutions on Azure
  • , Last Updated: Aug 09, 2026
  • Q & A: 93 Questions and Answers
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Microsoft AI-200 Exam Syllabus Topics:

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

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. Drag and Drop Question
A Python API running in ACA must send distributed traces to Azure Monitor.
The API creates spans. However, no traces appear in Azure Monitor.
You need to configure the OpenTelemetry SDK pipeline to export traces to Azure Monitor.
What should you do? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.


2. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
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.
You need to ensure that the API key remains outside of Git commit history and is available to the container at runtime.
Solution: Embed the API key as a hardcoded environment variable in the Dockerfile.
Does the solution meet the goal?

A) No
B) Yes


3. An application performs similarity search across 5 million embeddings stored in Azure Database for PostgreSQL with pgvector. Queries often filter by department before ranking by cosine distance.
P95 latency for vector similarity queries exceeds the SLA target. Monitoring shows sustained high CPU use during query execution.
You need to reduce P95 latency for filtered vector similarity queries.
What should you do?

A) Store embeddings as JSON.
B) Increase embedding dimensionality.
C) Create B-tree indexes on frequently filtered metadata columns.
D) Increase statement timeout.


4. Drag and Drop Question
A Python API retrieves a document from Azure Database for PostgreSQL by using a SQL statement. The API accepts the document ID from user input. The current implementation inserts the document ID directly into the SQL statement.
You need to secure the SQL statement execution by minimizing the possibility of SQL injection.
How should you modify the current implementation? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.


5. You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification. What should you implement?

A) Increase top_p to broaden token sampling
B) Use only the base model with no retrieval
C) Enable higher content filter severity
D) Configure the RAG pipeline to return retrieved document metadata (source, page) alongside generated answers and instruct the model to cite them


Solutions:

Question # 1
Answer: Only visible for members
Question # 2
Answer: A
Question # 3
Answer: C
Question # 4
Answer: Only visible for members
Question # 5
Answer: D

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