Average Salary of AI-900: Microsoft Azure AI Fundamentals Exam Certified Professionals
The average salary of an AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules Exam Certified Expert in:
- United States - 155,000 USD
- India - 11,42,117 INR
- Europe - 105,000 EURO
- England - 55,900 POUNDS
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Certification Path
The Azure AI Fundamentals certification might not be a prerequisite for any other certification levels, however, it can still be used to prepare for multiple Azure-related qualifications including the Microsoft Certified: Azure Data Scientist Associate and the Microsoft Certified: Azure AI Engineer Associate.
Microsoft Azure AI Fundamentals Exam Certification Details:
| Exam Price | $99 (USD) |
| Schedule Exam | Pearson VUE |
| Exam Code | AI-900 |
| Exam Name | Microsoft Certified - Azure AI Fundamentals |
| Duration | 60 mins |
| Books / Training | Course AI-900T00: Microsoft Azure AI Fundamentals |
| Passing Score | 700 / 1000 |
| Number of Questions | 40-60 |
| Sample Questions | Microsoft Azure AI Fundamentals Sample Questions |
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The benefit of obtaining the AI-900: Microsoft Azure AI Fundamentals Exam Certification
For people working in technology, having an Azure certification becomes important. The high demand for Azure certified experts, the high salary and benefits. Microsoft Azure AI Fundamentals Exam Certification give you skills and knowledge about a particular area of technology.
The benefit that you receive after passing the AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules is greater than the effort which you put in to pass this exam. Provide you a growth in terms of your knowledge and will open many opportunities for you. Sentiment of the IT companies will increase because of your skilled work. Candidates who are verified with the AI-900: Microsoft Azure AI Fundamentals Exam will get an advantage in job interviews. Sites such as Microsoft Learning comes with a collection of certifications, tips and articles which you can use to prepare for the exam.
Subjects of the test can be found on Microsoft Learning website. Clustering of data is one of the most important task on AI. It is used for improving accuracy. Labels are used to achieve this. Journey map is used to achieve this. Data grouping is used to achieve this. Validation is a process of finding what the probabilities are for the data to contain a given property. Remember to take the exam as early as possible.
Guides, strategies and testimonials you can find on the internet about the exam. Helpful to deal with the challenges in the exam. Attempt to use the free copies of the Microsoft Office Excel for this exam. Modeling approach for this exam is to use the AI technology through an artificial neural network.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Identify types of NLP solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines |
| Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer |
| Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe considerations for developing AI solutions - Describe responsible AI principles |



