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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question 1

Hotspot Question
You are building a semantic search feature for a chatbot. You store document embeddings in Redis.
You review the following Python code that connects to Redis and stores an embedding value:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Question 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 are using Azure Monitor Application Insights to investigate a production API. You open the Logs blade and set the time range to Last 24 hours.
An engineer recommends the following query to count requests by result code and sort the results from most frequent to least frequent:
requests
| summarize request_count = count() by resultCode
| order by request_count desc
You need to determine whether the query returns the number of requests grouped by result code and sorted from most to least frequent.
Solution: The query lists every individual request along with its result code.
Does the solution meet the goal?

A. Yes
B. No

Question 3

You need to add a custom voice that matches your company's brand for a text-to-speech feature, distinct from any standard neural voice. What should you use?

A. Azure AI Language sentiment analysis
B. Azure AI Speech Custom Neural Voice
C. Azure AI Translator
D. Azure AI Speech prebuilt neural voices

Question 4

Drag and Drop Question
You are implementing a Retrieval-Augmented Generation (RAG) system by using the native vector search capabilities of Azure Cosmos DB for NoSQL API.
You have a container named Documents that stores technical articles. Each article includes a property named embedding.
You must ensure that the system can perform efficient similarity searches between user queries and the stored articles.
You need to configure the database resources to support semantic retrieval.
Which configurations should you use? 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 developing an application that must extract structured field data (invoice number, total, vendor name) from scanned invoices in multiple layouts. You need a solution that requires no custom model training. What should you use?

A. Azure AI Document Intelligence prebuilt invoice model
B. Azure AI Document Intelligence custom neural model
C. Azure AI Language custom named entity recognition
D. Azure AI Vision Read API

Solutions: