DomainDescribe fundamental principles of machine learning on Azure
Exam standardSkills measured as of May 2, 2025
What this objective covers
Identify machine learning techniques and concepts and describe Azure Machine Learning capabilities. Official range: 15-20%. includes Identify regression machine learning scenarios.; Identify classification machine learning scenarios.; Identify clustering machine learning scenarios.; Identify features of deep learning techniques.; Identify features of the Transformer architecture.; Identify features and labels in a machine learning dataset.; Describe how training and validation datasets are used in machine learning.; Describe capabilities of automated machine learning.; Describe data and compute services for data science and machine learning.; Describe model management and deployment capabilities in Azure Machine Learning..
Why it matters
This objective contributes to the Describe fundamental principles of machine learning on Azure domain and represents knowledge or judgment expected within the AI-900 role. Prepare it as part of the wider domain rather than as an isolated fact list.
How to prepare
Define each term, connect it to the objective's practical decision, and use source material or hands-on work to test the concept. Finish with fresh, targeted questions and explain why the strongest alternative answer is weaker.
Objective area 1
Identify regression machine learning scenarios.
Study how Identify regression machine learning scenarios. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Study how Identify classification machine learning scenarios. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 3
Identify clustering machine learning scenarios.
Study how Identify clustering machine learning scenarios. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 4
Identify features of deep learning techniques.
Study how Identify features of deep learning techniques. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 5
Identify features of the Transformer architecture.
Study how Identify features of the Transformer architecture. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 6
Identify features and labels in a machine learning dataset.
Study how Identify features and labels in a machine learning dataset. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 7
Describe how training and validation datasets are used in machine learning.
Study how Describe how training and validation datasets are used in machine learning. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 8
Describe capabilities of automated machine learning.
Study how Describe capabilities of automated machine learning. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 9
Describe data and compute services for data science and machine learning.
Study how Describe data and compute services for data science and machine learning. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Objective area 10
Describe model management and deployment capabilities in Azure Machine Learning.
Study how Describe model management and deployment capabilities in Azure Machine Learning. supports the broader objective, then apply it in a AI-900 scenario instead of memorizing the phrase.
Keep this objective connected to its domain.
Domain-level review helps preserve the broader blueprint context.
