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Certification Topics of Microsoft AI-900 Exam
Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.
- Describe fundamental principles of machine learning on Azure (30-35%)
- Describe AI workloads and considerations (15-20%)
- Describe features of computer vision workloads on Azure (15-20%)
- Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
- Describe features of conversational AI workloads on Azure (15-20%)
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Prerequisites
This certification test has no official prerequisites. However, the interested candidates must develop their skills and knowledge in the domains of the exam topics. Although the individuals do not need this test to pursue more advanced Azure role-based options, they can gain extensive expertise while preparing for this exam. Your knowledge base can contribute to the success of more advanced certificates such as Microsoft Certified: Azure AI Engineer Associate or Microsoft Certified: Azure Data Scientist Associate.
Describing AI-900 Exam Facts and Topics
In general, AI-900 is a computer-based exam that’s available in English, Chinese, Japanese, Spanish, French, German, and Korean languages. It costs $99 for every attempt and can be scheduled with Certiport or Pearson VUE. Recently, the AI-900 exam curriculum has been updated to reflect the following skills as contained in the official study guide:
- Defining features of conversational AI workloads on Azure (15-20%).
- Describing fundamental principles of machine learning on Azure (30-35%);
- Designing features of NLP (Natural Language Processing) workloads on Azure (15-20%);
- Explaining features of computer vision workloads on Azure (15-20%);
- Detailing Artificial Intelligence considerations and workloads (15-20%);
Target Audience
The Microsoft AI-900 exam is designed for those individuals who have little to no experience in the world of IT. It is aimed at the students with both non-technical and technical backgrounds. They have basic programming experience and knowledge. However, they are not required to have software engineering or data science experience.
Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Describe considerations for developing AI solutions - Identify types of AI workloads |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe machine learning pipelines - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning |
| Topic 5: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision |
Microsoft Azure AI Fundamentals Sample Questions:
To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
In the context of Microsoft Azure AI Fundamentals (AI-900) and general machine learning principles, regression refers to a type of supervised learning used to predict continuous numerical values based on historical data. The goal of regression is to model the relationship between input variables (features) and a continuous output variable (target).
In this scenario, the task is to predict how many vehicles will travel across a bridge on a given day. The number of vehicles is a numerical value that can vary continuously depending on factors such as time of day, weather, weekday/weekend, or traffic trends. Because the output is numeric and not categorical, this problem type clearly fits into regression analysis.
Microsoft's official learning content for AI-900, under "Identify features of regression and classification machine learning models," specifies that regression models are used to predict values such as sales forecasts, demand estimation, temperature prediction, or traffic volume-all of which share the same underlying objective: predicting a quantity.
To clarify other options:
* Classification is used when predicting categories or discrete classes, such as determining whether an email is spam or not spam, or if an image contains a cat or a dog.
* Clustering is an unsupervised learning technique used to group similar data points without predefined labels (for example, grouping customers by purchasing behavior).
Since predicting the number of vehicles results in a continuous numerical output, it aligns precisely with the regression workload type described in the Microsoft AI-900 study materials.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service and has nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna-bot LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service
You are building a tool that will process images from retail stores and identify the products of competitors.
The solution will use a custom model.
Which Azure Cognitive Services service should you use?
- A. Custom Vision
- B. Face
- C. Form Recognizer
- D. Computer Vision
Correct Answer: A 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
Which AI service should you use to create a bot from a frequently asked questions (FAQ) document?
- A. Speech
- B. QnA Maker
- C. Language Understanding (LUIS)
- D. Text Analytics
Correct Answer: B 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
Stating the source of the data used to train a model is an example of which responsible Al principle?
- A. privacy and security
- B. transparency
- C. fairness
- D. reliability and safety
Correct Answer: B 🗳️
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
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