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Preparation Process

If you are planning to take the Microsoft AI-100 exam, it is very important that you prepare for it thoroughly. It doesn’t matter if you have a lot of experience in the field because if you don’t study for this certification test, there is a big chance that you will not be able to pass it. To prepare for the AI-100 exam, the first thing that every candidate needs to do is visit the official website. Here you will find a plethora of reliable resources that will convey all the authentic information to you. On this online platform, you will also find a few free learning paths that will allow you to understand all the details of your Microsoft test. These paths are as follows:

• Evaluating Text with Azure Cognitive Language Services
• Processing and Classifying Images with Azure Cognitive Vision Services
• Creating Intelligent Bots with the Azure Bot Service
• Processing and Translating Speech with Azure Cognitive Speech Services

After that, you can also enroll for the paid instructor-led training course available on the official website. As the name suggests, it is taught by a certified expert who knows everything about the topic. It is recommended that the learners use this resource as it will give them a chance to interact with the instructor and ask questions whenever they face difficulties.

One of the last things that every student needs to do is look for the valid practice tests. The Internet is littered with this training tool, so it is advised that the individuals take at least several of those tests. In this way, you will learn a lot about the exam questions and improve your time management skills.

Designing and Implementing an Azure AI Solution AI-100 Exam

Designing and Implementing an Azure AI Solution AI-100 Exam which is related to Microsoft Azure AI Solution and credits toward Microsoft Certified Azure AI Engineer Associate certifications. This exam validates the ability to use the various services within the Microsoft Azure Artificial Intelligence (AI) portfolio. Candidate must have a strong understanding of basic statistics, data ethics, data privacy, various aspects of the Microsoft Azure AI portfolio, related open source frameworks and technologies, and available data storage options. This exam verifies the Candidates have the ability to use their knowledge of cost models, capacity, and best practices to architect and implement Artificial Intelligence (AI) Solution using Microsoft Azure.

Reference: https://www.microsoft.com/en-us/learning/exam-ai-100.aspx

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Languages: English, Japanese, Chinese (Simplified), Korean

This exam measures your ability to accomplish the following technical tasks: analyze solution requirements; design solutions; integrate AI models into solutions; and deploy and manage solutions.

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Skills Measured

There are various topics that you will need to study to achieve success in the Microsoft AI-100 exam. All the domains included in this certification tests are as follows:

The first subject area that you will have to master is called Analyzing Solution Requirements. It makes up 25-30% of the exam content and consists of the following subdomains: recommending Azure Cognitive Services APIs to satisfy business requirements; mapping security requirements to tools, technologies, as well as processes; selecting the software, services, and storage necessary for supporting a solution.

The next section that you have to study is Designing an AI Solution that constitutes about 40-45% of the exam questions. It revolves around the subtopics, such as designing solutions that comprise one or more pipelines; designing solutions that utilize Cognitive Services; designing solutions that implement the Microsoft Bot Framework; designing the compute infrastructure to support a solution; designing for data governance, compliance, integrity, as well as security.

The last domain included in the Microsoft AI-100 exam is titled Implementing & Monitoring AI Solutions. It makes up the remaining 25-30% of the whole content. The subtopics covered here are the following: implementing an AI workflow; integrating AI services & solution elements; monitoring and assessing the AI environment.