AI-900: Microsoft Azure AI Fundamentals Study Guide
Published Feb 09 2023 12:04 AM 19.9K Views
Microsoft

With recent announcements about ChatGPT and OpenAI being integrated into services such as Bing and Microsoft Edge, and app development assistant solutions like Copilot from GitHub, Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the software development landscape, making them essential skills for students who want to be at the forefront of this change. The "AI-900: Microsoft Azure AI Fundamentals" exam provides candidates with an excellent opportunity to demonstrate their understanding of AI and ML concepts, as well as their familiarity with associated Microsoft Azure services.

 

Students having a basic understanding of cloud computing and client-server applications will have an advantage, although experience in data science and software engineering is not essential. This study guide will assist students in understanding what to expect on the exam, as well as the topics covered and extra resources. 

 

Raccoon students studying for the AI-900 examRaccoon students studying for the AI-900 exam

 

What to Expect on the Exam

The Exam AI-900 measures the learner’s knowledge of AI and ML concepts, as well as related Microsoft Azure services. The exam consists of 40-60 questions and lasts for 180 minutes. You may encounter multiple-choice questions, as well as drag-and-drop and hot area active screen questions. The topics covered in the exam include:
 

  • Describing AI Workloads and Considerations (20-25%): Learners will be evaluated on their ability to recognize elements of popular AI workloads such as anomaly detection, computer vision, natural language processing, and knowledge mining in this section of the exam. They will also be examined on their awareness of responsible AI guiding principles, such as fairness, dependability, privacy, inclusion, openness, and accountability.
     

  • Describing Fundamental Principles of Machine Learning on Azure (25-30%): Students must demonstrate their understanding of common machine learning types, such as regression, classification, and clustering, in this area of the exam. They will also need to comprehend basic machine learning concepts such as features and labels in a dataset, training and validation datasets, and the capabilities of Azure Machine Learning Studio's visual tools.
     

  • Describing Features of Computer Vision Workloads on Azure (15-20%): Learners will be tested on their grasp of popular types of computer vision solutions, such as picture classification and object detection, in this section of the exam. They'll also need to know how Azure services like Azure Cognitive Services assist computer vision workloads.
     

  • Describing Features of Natural Language Processing (NLP) Workloads on Azure (25-30%): Students will be evaluated on their comprehension of NLP workloads, including popular NLP tasks like sentiment analysis and text categorization, in this section of the exam. They'll also need to know how Azure services like Azure Cognitive Services support NLP workloads.
     

Study Resources

To help students prepare for the AI-900 exam, Microsoft provides a number of resources, including:

 

  • Microsoft Learn self-pace curriculum:
     
    • Microsoft Azure AI Fundamentals: Get started with artificial intelligence: Artificial intelligence (AI) enables incredible new ideas and experiences, and Microsoft Azure provides simple services to get you started.
       
    • Microsoft Azure AI Fundamentals: Explore visual tools for machine learning: Many modern apps and services rely on predictive machine learning models, which are at the heart of artificial intelligence. Learn how to construct and publish models in Azure Machine Learning without writing code.
       
    • Microsoft Azure AI Fundamentals: Explore computer vision: Computer vision is an artificial intelligence (AI) field in which software systems are designed to sense the world visually via cameras, pictures, and video. AI engineers and data scientists can handle a wide range of computer vision problems using a combination of bespoke machine learning models and platform-as-a-service (PaaS) solutions, including several cognitive services in Microsoft Azure.
       
    • Microsoft Azure AI Fundamentals: Explore natural language processing: Natural language processing enables applications to see, hear, communicate with, and comprehend users. Microsoft Azure makes it simple to create natural language apps by providing text analytics, translation, and word recognition services.
       
    • Microsoft Azure AI Fundamentals: Explore decision support: Learn how to use Azure to automate decision-making processes.
       
    • Microsoft Azure AI Fundamentals: Explore knowledge mining: Knowledge mining is an artificial intelligence (AI) discipline that uses a combination of cognitive services to quickly search and learn from massive amounts of data.
       
  • Instructor Led Course:
     
    • Course AI-900T00: Microsoft Azure AI Fundamentals: This course covers AI basics and Microsoft Azure services for AI solutions. The training raises awareness of popular AI workloads and Azure services that support them, not data scientists or software engineers. The course blends instructor-led instruction with Microsoft Learn online materials. The course's hands-on exercises are based on Learn modules, and students are encouraged to utilize Learn as a reference to reinforce and explore subjects.
       
  • Microsoft Documentation related to the AI-900 exam:
     
    • Azure Machine Learning - Learn how to use Azure Machine Learning to train and deploy models, as well as manage the ML lifecycle (MLOps). Tutorials, code samples, API references, and more are available.
       
    • Computer Vision - Developers can use complex image processing and information retrieval algorithms using the cloud-based Computer Vision API. Microsoft Computer Vision algorithms can interpret images depending on human inputs and image URLs. Quickstarts, tutorials, and examples teach visual content analysis.
       
    • Natural language processing technology (NLP) - Learn about sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
       
    • Speech Translation - Learn the advantages and possibilities of the voice translation service, which provides real-time, multi-language speech-to-speech and speech-to-text translation of audio streams.
       
    • Language Understanding (LUIS) - Learn how Language Understanding makes it possible for your applications to understand what a person wants in their own terms.
       
    • Speech-to-text - Real-time and batch transcription of audio streams into text, commonly known as speech recognition. It also allows for real-time pronunciation testing and provides feedback on the accuracy and fluency of spoken audio with extra reference text input.
       
    • Anomaly Detector API - Learn how to use the Anomaly Detector univariate and multivariate APIs to monitor data over time and discover anomalies using machine learning. Gain insight into your data, no matter the volume, industry, or scenario.
       
    • Azure Bot Service - There are several approaches to developing and deploying a chatbot. Learn how the Azure Bot Service delivers an integrated environment designed specifically for bot creation.

  • Free practice assessment: Microsoft offers free, multilingual Practice Assessments for the AI-900 exam. These Practice Assessments will give you an idea of the exam's style, language, and complexity. The exam's duration and difficulty are not reflected in these questions (e.g., you may see additional question types, multiple case studies, and labs) however they do provide examples to help you prepare for the exam.

 

Successfully completing the Microsoft Azure AI Fundamentals exam highlights your comprehension of AI concepts, machine learning, and Microsoft Azure services. This, in turn, will help you will gain the knowledge and skills you need to build AI solutions that are secure, reliable, and fair. Good luck on your exam and in we look forward to see your advancement in your AI software development career.
 

3 Comments
Co-Authors
Version history
Last update:
‎Feb 09 2023 05:15 AM
Updated by: