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Explain Computer Vision Workloads Features on Azure (15-20%)
Here, the following skills will be measured:
- Identify Azure services & tools for Computer Vision Projects – This section requires that the learners identify different capabilities of Computer Vision service, Face service, Customer Vision service, and Form Recognizer service.
- Identify the basic types of Computer Vision Solution – The candidates need to understand and be able to identify a range of features. They include image classification solutions, semantic segmentation solutions, object detection solutions, and optical attributes recognition solutions. It also covers the attributes of facial recognition, facial analysis, and facial detection solutions.
About Azure AI Fundamentals Certification and Who It Is Intended for
The Microsoft Certified: Azure AI Fundamentals qualification authenticates the learner's understanding and skills in AI & ML concepts along with associated Azure services. In particular, the individual with this certification is competent in demonstrating their understanding of various workloads related to those terms along with their implementation in Azure cloud computing services. In addition, this certification is an excellent way to get a step ahead in the preparation for other role-based Azure certificates as well as showcasing one's skill in the fundamentals of ML as well as AI. All in all, the Azure AI Fundamentals validation form Microsoft is for beginners and thus, doesn't require any technical knowledge and expertise like, for example, engineering for software. However, some understanding of programming will help you a lot to easily nail test AI-900.
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How to Register For Exam AI-900: Microsoft Azure AI Fundamentals?
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Describe the Basic Principles of ML on Microsoft Azure (30-35%)
As for this area, it includes the following:
- Identify the major tasks in crafting an ML solution – This subject area measures the students’ competence in explaining some concepts, such as basic features of data preparation and ingestion, model management and deployment, model training & evaluation, and feature selection and engineering.
- Explain the abilities of the No-code ML with Azure ML Learning Studio – This domain equips the individuals with the knowledge of the Azure ML designer and automated ML UI.
- Identify the basic ML types – This section equips the learners with the ability to identify various concepts, such as regression, clustering, and classification ML scenarios.
- Describe the ML core concepts – You should be able to identify the labels and features within a dataset for ML and describe the usage of validation datasets and training in ML. The applicants also need the ability to explain the usage of ML algorithms in model training, as well as interpret and choose the model evaluation yardsticks for regression and classification.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 2: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 3: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 4: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
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