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Test AI-900 Scope of Knowledge
Overall, AI-900 exam tests the applicant's understanding of AI and ML through various topics categorized into 5 domains:
- Workloads for Natural Language Processing (NLP) related to Azure (15-20%)
The fourth portion of AI-900 exam deals with Natural Language Processing or NLP workloads in Azure. To get a passing score, the entrant must have a firm grasp on the facets and usage of diverse NLP scenarios for workloads which comprise the extraction of the key phrase, the recognition of entities, the analysis of sentiments, modeling for languages, the recognition of speech, and translation traits. The applicants are also required to know about different techniques as well as services of Azure used for various NLP workloads. They are required to be capable of identifying diverse abilities of various services such as text analysis, LUIS, speech, and translator text.
- Conversational workloads for AI based on Azure and features (15-20%)
The fifth and final domain for such an exam tests the entrant's understanding of conversational AI workloads. This requires the comprehension of facets as well as cases for the usage of conversational traits for AI which includes web chatbots, voice menus for the telephone, and digital assistants used for personal purposes. The applicant also needs to know how to check various Azure services related to conversational AI. Additionally, they should have a firm understanding of the QnA Maker Service & Bot service. Finally, it is essential for candidates to know and be able to describe the common characteristics of conversational artificial intelligence solutions.
- Workloads for computer vision based on Azure and features (15-20%)
This segment of the official test is all about workloads for computer vision in relation to Azure services. To get through this part, one must have a general grasp of the various common forms of computer vision solutions that include image classification, object detection, semantic segmentation, recognition for the optical character, and facial detection. The applicants are also required to have an understanding of the diverse methods including services used in Azure for activities that utilize computer vision. Such methods and services include the vision for computers, vision for custom, services for face vision, and at last, the recognizer of the form.
- AI workloads and considerations (15-20%)
The first topic of the final exam deals with AI workloads that are usually common. It includes candidates' understating various types of workloads such as predicting forecasting traits, the detection of anomalies, vision for computer systems, NLP, and conversational AI workloads. The examinees are also required to have a thorough understanding of the principles of AI that possess responsibility traits. It means that the candidate needs to be well aware of all the considerations for different solutions for AI which include fairness, reliability, security, privacy, inclusiveness, transparency facets, and finally, functions for accountability.
- Fundamental principles of machine learning on Azure (30-35%)
In the second section, the applicant's knowledge of fundamental principles of machine learning in relation to Azure cloud computing services will be tested. This requires the applicant to have a clear comprehension of all the common types of various scenarios for ML that include classification, regression, and clustering. It is also essential to have a thorough understanding of the core MI concepts such as labels & features in datasets, dataset training that go with the validation process, etc. The entrant must as well as be capable of identifying the core task required in developing an ML solution. So, understanding the ingestion and preparation of data chunks, selection and engineering of various features, evaluation procedure as well as training for models, and deployment & management of those models will be vital. Finally, this portion of AI-900 exam includes concepts of ML automated user interface that are ML Wizard UI and Azure ML designer.
Microsoft AI-900 Exam Syllabus Topics:
| Topic | Details |
|---|---|
Describe Artificial Intelligence workloads and considerations (20-25%) | |
| Identify features of common AI workloads | - identify features of anomaly detection workloads - identify computer vision workloads - identify natural language processing workloads - identify knowledge mining workloads |
| Identify guiding principles for responsible AI | - describe considerations for fairness in an AI solution - describe considerations for reliability and safety in an AI solution - describe considerations for privacy and security in an AI solution - describe considerations for inclusiveness in an AI solution - describe considerations for transparency in an AI solution - describe considerations for accountability in an AI solution |
Describe fundamental principles of machine learning on Azure (25-30%) | |
| Identify common machine learning types | - identify regression machine learning scenarios - identify classification machine learning scenarios - identify clustering machine learning scenarios |
| Describe core machine learning concepts | - identify features and labels in a dataset for machine learning - describe how training and validation datasets are used in machine learning |
| Describe capabilities of visual tools in Azure Machine Learning studio | - automated machine learning - azure Machine Learning designer |
Describe features of computer vision workloads on Azure (15-20%) | |
| Identify common types of computer vision solution | - identify features of image classification solutions - identify features of object detection solutions - identify features of optical character recognition solutions - identify features of facial detection, facial recognition, and facial analysis solutions |
| Identify Azure tools and services for computer vision tasks | - identify capabilities of the Computer Vision service - identify capabilities of the Custom Vision service - identify capabilities of the Face service - identify capabilities of the Form Recognizer service |
Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%) | |
| Identify features of common NLP Workload Scenarios | - identify features and uses for key phrase extraction - identify features and uses for entity recognition - identify features and uses for sentiment analysis - identify features and uses for language modeling - identify features and uses for speech recognition and synthesis - identify features and uses for translation |
| Identify Azure tools and services for NLP workloads | - identify capabilities of the Language service - identify capabilities of the Speech service - identify capabilities of the Translator service |
| Identify considerations for conversational AI solutions on Azure | - identify features and uses for bots - identify capabilities of the Azure Bot service |
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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How to Prepare For AI-900: Microsoft Azure AI Fundamentals
Preparation Guide for AI-900: Microsoft Azure AI Fundamentals
Introduction
Microsoft AI-900 exam is designed for Microsoft partners who are certified by Microsoft to integrate Microsoft Azure Intelligent Solutions into their own products and solutions. The objective of the certification is to verify that approved partners have the knowledge and skills to deploy, configure, set up, activate, maintain, troubleshoot, and support all necessary Azure services. Microsoft AI-900 exam dumps exam tests Questions are designed to identify the exam taker level of competence in the functions, features, and services of Azure. The Microsoft AI-900 Certification Exam is a three to five-hour exam that covers the technologies necessary for building intelligent applications for cloud computing. Students should have a solid knowledge of Microsoft Azure IaaS, services, APIs, SQL Server 2017, and NET Framework. Courses are available online or in-person depending on individual needs.
This article will cover the areas of knowledge required for this exam, how many questions are covered in each area, and who is taking this certification exam.
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