Microsoft Microsoft Certified: Azure AI Fundamentals
- Exam: AI-900 (Microsoft Azure AI Fundamentals)
- Certification: Microsoft Certified: Azure AI Fundamentals
- Certification Provider: Microsoft
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The Microsoft Certified: Azure AI Fundamentals is for IT specialists who want to take advantage of Azure artificial intelligence and machine learning capabilities. If you want to have the foundational knowledge and skills in this area and want to launch your career in this fascinating field, this is the right qualification to get then.
Audience for Microsoft Certified: Azure AI Fundamentals Certificate
This is a certificate for candidates with tech & non-tech experience in Azure and its AI capabilities. Earning this certification means that you can work with machine learning and artificial intelligence workloads and know how to implement and integrate them with the Azure platform.
Similar to many IT certifications, there are no prerequisites that you’ll need to fulfill before you can enroll for your training. However, it is recommended that you have some programming experience as you’ll be using it as you work with AI and machine learning concepts. Additionally, candidates must pass one exam coded AI-900 to get the desired validation.
Microsoft AI-900 Exam Details
Before preparing for such an exam, you’ll need to know the finer details so you can do it effectively. The Microsoft AI-900 comprises 40 to 60 exam questions that you'll have to solve in 1 hour. You can expect a few question types such as multiple-choice, drag and drop, single answer, and many more. So, if you’re preparing, make sure to practice with these types of items. You’ll need to score at least 700 points out of 1000 for you to pass the final test. Finally, it costs $99 to attempt to take it.
Topics to Expect When Taking AI-900
As a candidate to sit for AI-900, you’ll need to have a good grasp of these skills and knowledge:
- Defining Various Workloads & Features for AI (15-20%)
To get into grips with Azure AI and machine learning capabilities, you’ll need to identify the most common AI workload features such as prediction, anomaly detection, computer vision, and others. These are the most common workloads, and you’ll have to know how each one behaves with the Azure platform. With many AI implications, you must know how to elaborate on the many guiding principles in developing responsible AI. This includes considerations used for reliability, safety, privacy, security, transparency, and accountability. So, every solution that you’ll create will follow these principles and considerations.
- Describing Basic Principles of Azure ML (30 to 35%)
Machine learning is another prominent feature of Azure AI. So, you’ll need to identify the most common types that you can implement in the platform and know a variety of scenarios like regression, classification, and clustering. Besides, you’ll have to outline fundamental ML concepts to create solutions capable of interpreting different features & labels within a dataset and describing how various algorithms are made use of. You’ll also need to understand the different basic tasks in providing an efficient ML solution. Commonly, Azure supports no-code machine learning using its capabilities. So, if you don’t have this experience yet, you should start with this and use the automated ML UI and designer.
- Highlighting Computer Vision Workloads and Their Peculiarities on Azure (15-20%)
Computer vision is another capability of Azure AI. Identifying standard solutions is essential to master the platform. Therefore, you’ll need to know how to identify image classification features, object detection, facial detection, and other functions to create sound solutions for your needs. You’ll also need to understand the many methods as well as tools for computer vision workloads like the Azure Computer Vision, Custom Vision, Face, and Form Recognizer services to make your work a lot easier.
- Shedding Light on NLP Workloads Based on Azure (15-20%)
Natural Language Processing or NLP is also another type of workload that’s supported by Azure. It comes with a few common scenarios like keyphrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition, speech synthesis, and translation. There are also a few NLP workloads that you should complete with Azure and whose capabilities you must be able to efficiently evaluate, such as a service for Text Analytics, LUIS, Speech service, and a service for Translating Texts. All in all, you’ll need to know how to implement these in your projects.
- Detailing Components of AI Workloads for Conversations on Azure (15 to 20%)
Lastly, you’ll need to understand the different features of conversational AI. Thus, you will identify various use cases like webchat bots, menus for telephone voice, and private digital assistants. This way, you’ll be able to identify common characteristics of conversational AI solutions and successfully implement them. There are also a few Azure services that you can integrate with conversational AI workloads, including a helpful service for the QnA Maker and the Bot Framework. Identifying these will allow you to use them in solutions that involve conversational AI.
Passing the Microsoft AI-900 earns you the Azure AI Fundamentals certification. It’s the perfect one to get if you want to become or expand your current role to an AI Engineer, AI Developer, or Data Scientist. These are all incredibly lucrative careers, where you, as an experienced AI Engineer, can average starting with $76k yearly, according to PayScale.com. Other positions, such as becoming an AI Developer, can earn you as much as $50k per annum. Some of the best Azure careers are in AI as businesses flock to cloud computing and artificial intelligence to streamline their processes and operations.
Likely Career Path
Since the Microsoft AI-900 test is one for the Microsoft Fundamentals certifications, you’ll surely have a lot of choices when it comes to your next steps. There are a lot of possible career pathways after passing such an exam and earning your certification. So, you can choose to opt for the Microsoft Certified: Azure Data Scientist Associate or the Microsoft Certified: Azure Developer Associate certificates. Likewise, you may want to opt for the Microsoft Certified: Azure AI Engineer Associate validation, which also will boost your acquired knowledge of AI. Overall, there is no shortage of choices if you’re committed to continuing on your path to becoming an Azure AI expert.
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- Defining Various Workloads & Features for AI (15-20%)
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