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To find the perfect AI-900 practice materials for the exam, you search and re-search without reaching the final decision and compare advantages and disadvantages with materials in the market. With systemic and methodological content within our practice materials, they have helped more than 98 percent of exam candidates who chose our AI-900 guide exam: Microsoft Azure AI Fundamentals before getting the final certificates successfully. Their great features can be briefly delved into following points.
Studying with Revision Books
Apart from the training endorsed by the certification vendor, it’s a good idea to exhaust the study guides from Amazon. Thus, here’s what you need to know about the two must-have revision books for the Microsoft AI-900 certification exam prep:
- Microsoft AI MVP Book: Practical Guide to Microsoft AI Written by 17 AI and Azure MVPs from all around the world
This book is a collection of skills and knowledge from a team of AI and Azure specialists with countless years of experience in the field. It is written by Microsoft Azure MVPs including Leila Etaati, Anupama Natarajan, Ashraf Ghonaim, Cameron Vetter, David Rendon, etc. who used their cumulative years of experience to design a definitive study guide and a solution for all trainees preparing for AI-900 exam. The authors state it categorically that this book does not intend to cover everything there’s to know about Microsoft AI. And that it is important to combine it with other study materials to have a better chance of passing the Microsoft AI-900 exam. In a nutshell, this guide targets AI and ML developers, architects, data scientists, .net developers, and consultants who are already familiar with the foundational AI concepts. Remember it is not your best guide if you are starting from a zero, but an excellent choice if you are already familiar with most of the concepts in this field.
- Azure AI Fundamentals: Study Guide and Practice Exam for Microsoft AI-900 Exam
This guide by David Voss is based on the current test curriculum and targets trainees looking to gain foundational knowledge of the AI solutions and services on Azure. It comprises practice test questions and explanations that are consistent with Microsoft’s online learning tools. So, if you want to prepare with confidence and pass your exam easily, get your Kindle copy today from Amazon for only around $12.
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:
- 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.
- 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.
- 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.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
Exam Details
The Microsoft AI-900 exam is the only qualifying test that leads to the Microsoft Certified: Azure AI Fundamentals certification. It measures one’s knowledge of AI considerations & workloads, features of NLP workloads on Azure, and basic principles of ML on Azure. The candidates can choose to take this exam in English, French, Simplified Chinese, Japanese, German, Spanish, or Korean. It contains 40-60 questions covering different types, including case studies, drag and drop, active screen, build list, and multiple choice. You can schedule your test with Certiport or Pearson VUE. However, it is required to pay the fee of $99 before scheduling it.
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Features of generative AI workloads on Azure | 20–25% | - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines - Describe core concepts of machine learning |
| Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions - Describe capabilities of Azure Translator |
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