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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question 1

Your application must classify uploaded product images into one of 40 custom categories specific to your business (e.g., proprietary part numbers). What should you use?

A. Azure AI Vision prebuilt image analysis
B. Azure AI Face API
C. Azure AI Custom Vision (image classification)
D. Azure AI Document Intelligence

Question 2

Your organization requires that all prompts and completions sent to Azure OpenAI be retained for zero data logging beyond what's required for abuse monitoring, per contractual requirements.
What should you do?

A. Disable Azure Monitor entirely
B. Store all data only in Cosmos DB
C. Request modified content filtering / abuse monitoring configuration through Microsoft for the resource
D. Use a lower model version

Question 3

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. lease container
B. strong consistency
C. autoscale throughput
D. indexing precision

Question 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.

Question 5

You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column.
The schema must support vector similarity search and reliable date filtering.
You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Use a typed timestamp column for created_at.
B. Store created_at as a free-form string.
C. Store embeddings in a pg vector column.
D. Store embeddings in a varchar column.

Solutions: