Exam AI-900: Microsoft Azure AI Fundamentals
Languages: English, Japanese, Chinese (Simplified), Korean, German, French,
Spanish, Portuguese (Brazil), Russian, Indonesian (Indonesia), Arabic (Saudi
Arabia), Chinese (Traditional), Italian
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
The English language version of this exam will be
updated on April 29, 2022. Please download the exam skills outline below to see
what’s changing.
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%)
The following table shows the changes that will
be implemented on April 29, 2022 to the English language version of this exam.
Following the comparison table, the revised exam guide is included.
| Old objective number | Subtask changes and new location |
| 1.1 identify features of common AI workloads revised subtasks | revised subtasks |
| 2.2 describe core machine learning concepts | revised subtasks |
| 2.3 identify core tasks in creating a machine learning solution | deleted |
| 2.4 describe capabilities of no code machine learning with Azure Machine Learning Studio | revised title and subtasks; new 2.3 |
| 4.2 identify Azure tools and services for NLP workloads | revised subtasks |
| 5.1 describe features of conversational AI workloads on Azure | deleted: revised subtasks: moved to 4.3 |
| 5.2 identify Azure services for conversational AI | deleted; revised subtask: moved to 4.3 |
Audience Profile
This exam is an opportunity to demonstrate knowledge of machine learning
(ML) and artificial intelligence (AI) concepts and related Microsoft Azure
services. Candidates for this exam should have familiarity with AI-900’s
self-paced or instructor-led learning material.
This exam is intended for candidates with both technical and non-technical
backgrounds. Data science and software engineering experience are not required;
however, awareness of cloud basics and client-server applications 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 follow each of the skills measured are intended to
illustrate how we are assessing that skill. Related topics may not be covered in
the exam.
NOTE: Most questions cover features that are general availability (GA). The exam
may contain questions on Preview features if those features are commonly used.
Describe Artificial Intelligence workloads and considerations (20—25%)
Identify features of common AI workloads
• identify features of anomaly detection workloads
• identify computer vision workloads
• identify natural language processing workloads
• identify knowledge mining 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 (25—30%)
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 capabilities of visual tools in Azure Machine Learning Studio
• automated machine learning
• 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 optical character recognition solutions
• identify features of facial detection, facial recognition, and facial 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
(25—30%)
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 Language service
• identify capabilities of the Speech service
• identify capabilities of the Translator service
Identify considerations for conversational AI solutions on Azure
• identify features and uses for bots
• identify capabilities of the Azure Bot service
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
Answer: B
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.
Answer: B
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.
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
Answer: B
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