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Microsoft Certified: Azure AI Engineer Associate Certification Facts
The Microsoft Certified: Azure AI Engineer Associate certification requires candidates to be capable of working with Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
About Actual Test
The AI-100: Designing and Implementing an Azure AI Solution certification exam is available in English, Japanese, Korean, and Simplified Chinese. Plus, the registration and scheduling for the official exam are handled by Pearson VUE. To know more, the test will cost USD 165, and in the main evaluation, applicants will have to solve around 40-60 questions in just 130-150 minutes. At last, the passing rate for AI-100 is 700 points.
Intended Audience & Topics Tested
The AI-100 exam, in particular, is designed for determined Azure AI engineers with a fundamental understanding of statistics, data privacy, and ethics. The individuals eager to pass this certification exam should also have an understanding of Azure AI technologies, and all the relevant frameworks as well as data storage solutions. Moreover, the applicant should have a thorough grasp of cost models and the implementation of AI solutions through Azure. Overall, the examinees should demonstrate proficiency in three topics namely:
- Analyzing the requirements for solutions;
- Designing the solutions of AI;
- Executing as well as monitoring the solutions of AI.
The first tested domain is scattered around such objectives as offering Azure Cognitive Services APIs in order to meet the requirements of businesses, mapping security tools, processes, and technologies, and how to appropriately choose the services, software, and storage so that it was possible to support a particular solution. In more detail, within such a scope, the candidates are required to be familiar with AI models, architecture, and services. Plus, understanding service endpoints and determining the components required to connect them is also crucial to get through this domain. As you remember, after that, the entrant should also be fluent in mapping security requirements. This means examinees need to be capable of determining various processes and regulations required in conforming to data privacy as well as other relevant needs. In addition, the applicants should be able to determine the users and groups that grant them access to relevant data alongside interfaces. Furthermore, understanding Azure Cognitive Services output, storage logging, bot state data, integration points, and auditing requirements are some of the areas that were elaborated within this topic.
The second part scrutinizes the appropriate wait to design AI solutions. In all, it sheds light on 5 chapters, among which you’ll encounter how to define various solutions that comprise one or several pipelines, how to sketch solutions by making use of Cognitive Services, how to create solutions that deploy the Microsoft Bot Framework, the way to outline the compute architecture in order to support a peculiar solution, and finally, how to design for the governance of data, integrity compliance, and security. In particular, these objectives will throw light on designing an AI application workflow, dealing with the ingest & egress data, defining pipelines with the help of AI applications, and choosing the AI tool that will meet the constraints of costs. What is more, candidates must be aware of how to create various solutions that can utilize speech, language, vision, search, and knowledge. After that, dealing with Microsoft Bot Framework includes being able to merge AI solutions with bots, designing bot services using LUIS, and integrating bots with channels as well as Azure app services & Azure Application Insights. Then, the entrants are also required to be skilled in determining the need for GPU, FPGA, and CPU-based solutions and need to be capable of specifying the type of compute the solution to use that also satisfies the cost constraints. In addition to all these, the applicant must be equipped with the knowledge to define the compute infrastructure that is cloud-based, on-premises, or hybrid, decide on how to authenticate users to AI services, govern the related data appropriately, deal with data privacy policies, and create a content moderation strategy within the AI solution.
Finally, the last tested domain is divided into three sections that look at the way to put an AI workflow into operation, how to unite the components of the solution together with AI services, and how to evaluate as well as manage the setting of AI. Thus, the applicant should be fluent in integrating and configuring the required components necessary for the usage of Azure Cognitive Services APIs, and also possess expertise in configuring integration with Azure Cognitive Services and Microsoft Bot Framework. Besides, candidates must be knowledgeable about how to infuse the Azure Cognitive Search in a particular solution, learn the differences between reported metrics & KPIs, and the way to create interfaces for custom AI services.
Passing the Microsoft AI-100 exam and earning the Microsoft Certified: Azure AI Engineer Associate certification may be difficult but it also has its benefits. A bump in salary and getting in reach with high-paying jobs are some of them. For example, certified individuals may opt to become Azure AI Engineers with an average annual salary of $134k as stated by ZipRecruiter.
The best course of action after passing the AI-100 exam and earning the Microsoft Certified: Azure AI Engineer Associate designation would be to go for the Microsoft Certified: Azure Solutions Architect Expert, Microsoft Certified: DevOps Engineer Expert, and Microsoft Certified: Power Platform Solution Architect Expert validations to further expand your reach.