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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-900
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:
- Designing features of NLP (Natural Language Processing) workloads on Azure (15-20%);
- Describing fundamental principles of machine learning on Azure (30-35%);
- Explaining features of computer vision workloads on Azure (15-20%);
- Detailing Artificial Intelligence considerations and workloads (15-20%);
- Defining features of conversational AI workloads on Azure (15-20%).
The Microsoft AI-900 exam, usually known as the Microsoft Azure AI Fundamentals, is well suited to baseline-level IT specialists with knowledge of artificial intelligence (AI) & Machine Learning (ML), and the relevant Microsoft Azure concepts. This is the only test that you should pass to qualify for the Microsoft Certified: Azure AI Fundamentals certification.
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For more info read reference:
Microsoft AI-900 Official Certification Learning Site
Training and Certification Guide
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Microsoft AI-900 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe use cases for generative AI - Describe responsible AI practices for generative AI |
| Topic 2: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Identify types of NLP solutions |
| Topic 3: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision |
| Topic 4: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
| Topic 5: Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe machine learning pipelines |
Microsoft Azure AI Fundamentals Sample Questions:
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
This question tests your understanding of machine learning workflow tasks as described in the Microsoft Azure AI Fundamentals (AI-900) study guide and the Microsoft Learn module "Explore the machine learning process." The AI-900 curriculum divides the machine learning lifecycle into key phases: data preparation, feature engineering and selection, model training, model evaluation, and model deployment. Each phase has specific tasks designed to prepare, build, and assess predictive models before deployment.
* Examining the values of a confusion matrix # Model evaluationIn Azure Machine Learning, evaluating a model involves checking its performance using metrics such as accuracy, precision, recall, and F1- score. The confusion matrix is one of the most common tools for this purpose. According to Microsoft Learn, "model evaluation is the process of assessing a trained model's performance against test data to ensure reliability before deployment." Analyzing the confusion matrix helps determine whether predictions align with actual outcomes, making this task part of model evaluation.
* Splitting a date into month, day, and year fields # Feature engineeringFeature engineering refers to transforming raw data into features that better represent the underlying patterns to improve model performance. The study guide describes it as "the process of creating new input features from existing data." Splitting a date field into separate numeric fields (month, day, year) is a classic example of feature engineering because it enables the model to learn from temporal patterns that might otherwise remain hidden.
* Picking temperature and pressure to train a weather model # Feature selectionFeature selection involves identifying the most relevant variables that have predictive power for the model. As defined in Microsoft Learn, "feature selection is the process of choosing the most useful subset of input features for training." In this scenario, selecting temperature and pressure variables as inputs for a weather prediction model fits perfectly within the feature selection stage.
Therefore, the correct matches are:
# Examining confusion matrix # Model evaluation
# Splitting date field # Feature engineering
# Picking temperature & pressure # Feature selection
You have a frequently asked questions (FAQ) PDF file.
You need to create a conversational support system based on the FAQ.
Which service should you use?
- A. Text Analytics
- B. Language Understanding (LUIS)
- C. QnA Maker
- D. Computer Vision
Correct Answer: C 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?
- A. Ensure that a training dataset is representative of the population.
- B. Ensure that all visuals have an associated text that can be read by a screen reader.
- C. Provide documentation to help developers debug code.
- D. Enable autoscaling to ensure that a service scales based on demand.
Correct Answer: C 🗳️
Explanation: Only visible for Dumpexams members. You can sign-up / login (it's free).
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
This question refers to a system that monitors a user's emotions or expressions-in this case, identifying whether a kiosk user is annoyed-through a video feed. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify Azure services for computer vision," this scenario falls under facial analysis, which is a capability of Azure AI Vision or the Face API.
Facial analysis involves detecting human faces in images or video and analyzing facial features to interpret emotions, expressions, age, gender, or facial landmarks. The AI model does not try to identify who the person is but rather interprets how they appear or feel. For example, facial analysis can detect emotions such as happiness, anger, sadness, or surprise, which allows applications to infer a user's engagement or frustration level while interacting with a system.
Option review:
* Face detection: Identifies the presence and location of a face in an image but does not interpret expressions or emotions.
* Facial recognition: Matches a detected face to a known individual's identity (for authentication or security), not for emotion detection.
* Optical character recognition (OCR): Extracts text from images or scanned documents and has no relation to human emotion or facial features.
Therefore, determining whether a kiosk user is annoyed, happy, or frustrated involves emotion detection within facial analysis, making Facial analysis the correct answer.
This aligns with AI-900's definition of computer vision workloads, where facial analysis provides insights into emotions and expressions, supporting user experience optimization and customer behavior analytics.
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Identity the retailer from a receipt.
- B. Translate a form from French to English.
- C. Extract the invoice number from an invoice.
- D. Find image of product in a catalog.
Correct Answer: A,C 🗳️
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