Microsoft Certified: Azure AI Fundamentals AI-900
Enhance your certification score. Even though you're working at a top company, you'll want to keep your certification score up. It's important for your job search, especially if you want to go for a higher level position. When you're looking for new jobs, people are going to be screening your resume a bit with certifications.
Understand the test structure and what to expect; then walk through each topic area, quiz yourself with practice questions and answers, and ensure you’re ready to take the certification.
Practice tests are created by Subject Matter Experts. Your results are immediately available, while you stay focused on your exam results. Practice tests provides the answer to a test/questions you haven't already learned.
Exam Questions similar to actual Certification Exam.
Life time Access to practice tests to try as many times until you master the subject. You have access to practice test answers 24 hours a day, 365 days a year. If you're not satisfied, you can easily return to the practice test to make corrections.
The practice test have been designed carefully by maintaining the exam structure , syllabus, topic weights , cut score and time duration same as actual certification exam.
The aim of the practice exam is to allow the candidate to identify the risks related to a exam topics and be able to recognise them when analysing a real practice scenario.
Applicants will need to make sure that they are able to complete the practice examination in its entirety and pass all the multiple choice tests in general nature.
Overview of Artificial Intelligence
Artificial intelligence (AI) is the development of software that mimics human actions and skills. Key features include:
Machine learning - This is how we "train" a computer model to make predictions and draw conclusions from data, and it is frequently the foundation for an AI system.
Anomaly detection is the capacity to identify mistakes or unexpected behaviour in a system automatically.
Computer vision is the capacity of software to use cameras, video, and photographs to interpret the world visually.
Natural language processing is the capacity of a computer to understand and respond to written or spoken language.
Conversational AI - A software "agent's" capacity to participate in a conversation.
Machine Learning in Azure
The majority of AI solutions rely on machine learning.
Microsoft Azure provides the following services: Azure Machine Learning Service - A cloud-based platform for developing, managing, and deploying machine learning models. The following capabilities and functionalities are available in Azure Machine Learning:
Automated machine learning: With this functionality, non-experts may easily develop machine learning models from data
Azure Machine Learning Designer is a no-code interface for creating machine learning solutions.
Professional data scientists have access to cloud-based data storage and computational resources to execute code for large-scale data research.
Pipelines: Pipelines may be created by software engineers, data scientists, and IT operations experts to handle model deployment, training, and management.
What you'll discover
Obtain AI-900: Azure AI Fundamentals certification.
It may be used as a pre-assessment to determine which areas of your learning need to be addressed.
Other Azure role-based certifications, such as Azure Data Scientist Associate or Azure AI Engineer Associate, can be utilised to prepare for Azure AI Fundamentals, however
This course is ideal for students seeking the Azure IA basics certification.
The exam has 40 to 60 questions with a timeline of 60 minutes.
The exam contains many different question types.
A passing grade is around 70%.
Skills measured in AI-900: Microsoft Azure AI Fundamentals
The English language version of this exam was updated on April 29, 2022.
Describe Artificial Intelligence workloads and considerations (20-25%)
Describe fundamental principles of machine learning on Azure (25-30%)
Describe features of computer vision workloads on Azure (15-20%)
Describe features of Natural Language Processing (NLP) workloads on Azure (25-30%)
