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Microsoft AI-100 is intended for the candidates looking to validate their expertise in the use of cognitive services, knowledge mining, and machine learning. This is a qualifying exam for the Microsoft Certified: Azure AI Engineer Associate certification. The potential candidates must be able to use their ability to implement and architect Microsoft AI solutions. These solutions cover computer vision, conversational AI, speech, and language processing.
The target candidates for the Microsoft AI-100 exam are Azure AI Engineers. These professionals have the responsibility of analyzing the prerequisites for AI solutions. They also recommend the relevant technologies and tools as well as design and implement AI solutions. These solutions should meet the established performance and scalability requirements. The intended audience should be able to work with the Data Engineers, Software Developers, Data Scientists, and IoT Specialists to develop full end-to-end solutions. They also translate the vision developed by Solution Architects.
You are required to have the relevant skills, experience, and knowledge in the design and implementation of the AI agents and apps that utilize different solutions. These include Microsoft Azure Cognitive Services, data storage in Azure, Azure Cognitive Search, and Azure Bot Service. Additionally, the applicants need to have the ability to suggest solutions that utilize open source technologies. They should also possess a good understanding of the components that structure the Azure AI portfolio. It is also important to have an understanding of available data storage alternatives. The students have to know when the customized API needs to be developed to fulfill the established prerequisites.
The potential candidates can take the Microsoft AI-100 exam in English, Korean, Simplified Chinese, or Japanese. The fee for this test is $165, and you can schedule it with Pearson VUE. You can expect an average of 40-60 questions, which cover different types. Some of these are active screen, build list, short answer, best answer, case studies, and multiple choice. The passing score for the exam is 700 points on a scale of 100-1000.
Microsoft AI-100 focuses on the specific skills that the individuals are expected to develop while preparing for this test. These skills are what the certification exam will measure in its topics, and these are highlighted below:
Evaluate Solution Prerequisites: 25-30%
The candidates should be able to suggest the Azure Cognitive Services APIs that can meet the business prerequisites. This means that they must be able to demonstrate their skills in the following areas:
- Choose the appropriate processing architecture for solutions;
- Choose the relevant technologies for data processing;
- Select the relevant AI services and models;
- Establish technologies and components needed to connect the service endpoints;
- Establish automation prerequisites.
You should also be able to map out the security prerequisites to technologies, processes, and tools. The following are the skills that will be evaluated in this regard:
- Establish regulations and processes required to match with regulatory requirements, data protection, and data privacy;
- Establish the specific groups and users that have access to interfaces and information;
- Establish the relevant tools for solutions;
- Establish auditing prerequisites.
The examinees should also be able to choose software, storage, and services needed for support of a solution. The skills to be measured in this subtopic include the following:
- Establish the relevant tools and services for solutions;
- Establish the integration points with other Microsoft services;
- Establish storage needed to store bot state data, Azure Cognitive Service output, and logging.
Design AI Solutions: 40-45%
This section requires that the test taers have competence in designing solutions that entail at least one pipeline. The skills that this topic area measures include the following:
- Describe the workflow process for AI applications;
- Design the strategy for egress and ingest data;
- Design integration points between pipelines and multiple workflows;
- Design pipelines that utilize AI apps;
- Choose AI solutions that fulfill cost constraints;
- Design pipelines that recognize Azure Machine Learning Models.
The learners should also be able to design different solutions that utilize Cognitive Services. They should have competence in designing solutions that utilize anomaly detection APIs, vision, language, search, knowledge, and speech. This domain also measures one’s skills in designing the solutions that can implement Microsoft Bot Frameworks. They are as follows:
- Incorporate AI solutions and bots;
- Design bot services that utilize LUIS (Language Understanding);
- Incorporate bots with Azure Application Insights and Azure app services;
- Design bots that can integrate with different channels.
Besides that, you should be able to design compute infrastructures that can support solutions. The skills to be measured in this regard include the following:
- Establish whether to create a CPU, GPU, or FGPA-based solution;
- Establish whether to use on-premises, Cloud-based, or hybrid compute infrastructure;
- Choose compute solutions that fulfill cost constraints.
The students also need competence in designing for data governance, security, integrity, and compliance. These cover the skills in the following:
- Define how applications and users authenticate with AI services;
- Design the content moderation strategy for the usage of data in AI solutions;
- Design the strategy that ensures that solutions meet the industry standards and data privacy regulations.
Monitor & Implement AI Solutions: 25-30%
This subject area requires that the candidates understand how to implement AI workflows. It evaluates the skills in the following domains:
- Develop AI pipelines;
- Manage flows of data across solution components;
- Create solutions endpoints;
- Execute the processes for data logging;
- Develop a streaming solution.
The potential candidates also need the skills in integrating AI solutions and services components, including:
- Input datasets and configure the requirement components to enable Azure Cognitive Services APIs consumption;
- Configure Azure Cognitive Services with integration;
- Implement Azure Cognitive Search within a solution.
Finally, you should be able to evaluate and monitor AI environments. The skills that are required in this topic include the following:
- Establish the differences between reported metrics and KPIs as well as the causes for these differences;
- Establish the differences between actual and expected workflow throughput;
- Monitor the AI elements for availability;
- Maintain AI solutions for progressive improvement;
- Suggest changes for AI solutions based on performance data.
Those individuals who pass the Microsoft AI-100 exam will get the associate-level certification that brings a lot of benefits. This can open up various career opportunities for the candidates. Thus, some of the job titles that you can pursue with this certificate include an AI Engineer, a Customer Engineer, a Software Engineer, an AI Strategy Architect, a Cloud Solution Architect, and a Technical Program Manager. The average salary for these positions is $97,000 per year.
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