Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.
Part of the requirements for: Microsoft Certified: Azure AI Fundamentals
Related exams: none
Important: See details
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Exam AI-900: Microsoft Azure AI Fundamentals
Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish
Retirement date: none
Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.
Skills measured
Describe AI workloads and considerations (15-20%)
Describe fundamental principles of machine learning on Azure (30-35%)
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%)
Exam AI-900: Microsoft Azure AI Fundamentals – Skills Measured
Audience Profile
Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it is not a prerequisite for any of them.
Skills Measured
NOTE: The bullets that appear below each of the skills measured are intended to illustrate how we are assessing that skill. This list is not definitive or exhaustive.
NOTE: In most cases, exams do NOT cover preview features, and some features will only be added to an exam when they are GA (General Availability).
Describe Artificial Intelligence workloads and considerations (15-20%)
Identify features of common AI workloads
. identify prediction/forecasting workloads
. identify features of anomaly detection workloads
. identify computer vision workloads
. identify natural language processing or knowledge mining workloads
. identify conversational AI workloads
Identify guiding principles for responsible AI
. describe considerations for fairness in an AI solution
. describe considerations for reliability and safety in an AI solution
. describe considerations for privacy and security in an AI solution
. describe considerations for inclusiveness in an AI solution
. describe considerations for transparency in an AI solution
. describe considerations for accountability in an AI solution
Describe fundamental principles of machine learning on Azure (30-35%)
Identify common machine learning types
. identify regression machine learning scenarios
. identify classification machine learning scenarios
. identify clustering machine learning scenarios
Describe core machine learning concepts
. identify features and labels in a dataset for machine learning
. describe how training and validation datasets are used in machine learning
. describe how machine learning algorithms are used for model training
. select and interpret model evaluation metrics for classification and regression
Identify core tasks in creating a machine learning solution
. describe common features of data ingestion and preparation
. describe common features of feature selection and engineering
. describe common features of model training and evaluation
. describe common features of model deployment and management
Describe capabilities of no-code machine learning with Azure Machine Learning:
. automated Machine Learning tool
. azure Machine Learning designer
Describe features of computer vision workloads on Azure (15-20%)
Identify common types of computer vision solution:
. identify features of image classification solutions
. identify features of object detection solutions
. identify features of semantic segmentation solutions
. identify features of optical character recognition solutions
. identify features of facial detection, recognition, and analysis solutions
Identify Azure tools and services for computer vision tasks
. identify capabilities of the Computer Vision service
. identify capabilities of the Custom Vision service
. identify capabilities of the Face service
. identify capabilities of the Form Recognizer service
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
Identify features of common NLP Workload Scenarios
. identify features and uses for key phrase extraction
. identify features and uses for entity recognition
. identify features and uses for sentiment analysis
. identify features and uses for language modeling
. identify features and uses for speech recognition and synthesis
. identify features and uses for translation
Identify Azure tools and services for NLP workloads
. identify capabilities of the Text Analytics service
. identify capabilities of the Language Understanding Intelligence Service (LUIS)
. identify capabilities of the Speech service
. identify capabilities of the Text Translator service
Describe features of conversational AI workloads on Azure (15-20%)
Identify common use cases for conversational AI
. identify features and uses for webchat bots
. identify features and uses for telephone voice menus
. identify features and uses for personal digital assistants
Identify Azure services for conversational AI
. identify capabilities of the QnA Maker service
. identify capabilities of the Bot Framework
QUESTION 1
A company employs a team of customer service agents to provide telephone and email support to customers.
The company develops a webchat bot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the webchat bot solution?
A. increased sales
B. a reduced workload for the customer service agents
C. improved product reliability
Correct Answer: B
Section: Describe Artificial Intelligence workloads and considerations
Explanation
Explanation/Reference:
QUESTION 2
For a machine learning progress, how should you split data for training and evaluation?
A. Use features for training and labels for evaluation.
B. Randomly split the data into rows for training and rows for evaluation.
C. Use labels for training and features for evaluation.
D. Randomly split the data into columns for training and columns for evaluation.
Correct Answer: D
QUESTION 3
You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
A. Set Validation type to Auto.
B. Enable Explain best model.
C. Set Primary metric to accuracy.
D. Set Max concurrent iterations to 0.
Correct Answer: B
QUESTION 4
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?
A. fairness
B. inclusiveness
C. reliability and safety
D. accountability
Correct Answer: B
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