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Microsoft AI-900 Exam Questions
- Topic 1: Describe Artificial Intelligence workloads and considerations: It identifies specifications of common AI workloads and guiding principles for responsible AI.
- Topic 2: Describe fundamental principles of machine learning on Azure: It describes core machine learning concepts and Azure Machine Learning capabilities. Moreover, the topic also delves into identifying common machine-learning techniques.
- Topic 3: Describe features of computer vision workloads on Azure: This topic discusses identifying common types of computer vision solution, Azure tools and services for computer vision tasks.
- Topic 4: Describe features of Natural Language Processing (NLP) workloads on Azure: In this topic, questions about common NLP Workload Scenarios features, Azure tools, and services for NLP workloads appear.
- Topic 5: Describe features of generative AI workloads on Azure: Features of generative AI solutions and capabilities of Azure OpenAI Service are discussed in this topic.
Free Microsoft AI-900 Exam Actual Questions
Note: Premium Questions for AI-900 were last updated On Aug. 13, 2025 (see below)
What should you do to reduce the number of false positives produced by a machine learning classification model?
You build a QnA Maker bot by using a frequently asked questions (FAQ) page.
You need to add professional greetings and other responses to make the bot more user friendly.
What should you do?
You need to generate cartoons for use in a brochure. Each cartoon will be based on a text description.
Which Azure OpenAI model should you use?
Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE; Each correct selection is worth one point.
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is.
This is an example of which type of natural language processing workload?
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
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