AI-Fundamentals
Curriculum
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Identify features of common AI workloads
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Identify guiding principles for responsible AI
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Describe considerations for fairness in an AI solution
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Describe considerations for reliability and safety in an AI solution
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Describe considerations for privacy and security in an AI solution
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Describe considerations for inclusiveness in an AI solution
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Describe considerations for transparency in an AI solution
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Describe considerations for accountability in an AI solution
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Describe fundamental principles of machine learning on Azure (30—35%) Identify common machine learning types
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Identify regression machine learning scenarios
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Identify classification machine learning scenarios
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Identify clustering machine learning scenarios
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Describe core machine learning concepts
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Identify features and labels in a dataset for machine learning
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Describe how training and validation datasets are used in machine learning
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Describe how machine learning algorithms are used for model training
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Select and interpret model evaluation metrics for classification and regression
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Identify core tasks in creating a machine learning solution
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Describe capabilities of no-code machine learning with Azure Machine
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Describe features of computer vision workloads on Azure (15—20%)
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Identify common types of computer vision solution:
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Identify features of image classification solutions
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Identify features of object detection solutions
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Identify features of optical character recognition solutions
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Identify features of facial detection, facial recognition, and facial analysis solutions
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Identify Azure tools and services for computer vision tasks
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Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
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Identify features of common NLP Workload Scenarios
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Identify features and uses for key phrase extraction
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Identify features and uses for entity recognition
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Identify features and uses for sentiment analysis
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Identify features and uses for language modeling
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Identify features and uses for speech recognition and synthesis
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Identify features and uses for translation
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Identify Azure tools and services for NLP workloads
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Describe features of conversational AI workloads on Azure (15—20%)
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Identify features of common AI workloads
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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
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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 capabilities of no-code machine learning with Azure Machine
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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
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Identify Azure tools and services for computer vision tasks
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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
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Identify Azure tools and services for NLP workloads
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Describe features of conversational AI workloads on Azure (15—20%)
Overview
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.
• Describe Artificial Intelligence 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%)
Describe Artificial Intelligence workloads and considerations (15—20%)
Certification
Benfits of NSDC certification
- The course content has been vetted and approved by the NSDC, meaning it aligns with industry standards and has a good chance of leading to employment.
- The training center has met certain quality standards, such as having a certain number of trainers and equipment.
- You may be eligible for a loan from the NSDC to pay for the course.
- You may be able to get a certificate recognized by the government and industry.
- You may be able to get placement assistance after completing the course
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.
• Describe Artificial Intelligence 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%)
Describe Artificial Intelligence workloads and considerations (15—20%)
Certification
- The course content has been vetted and approved by the NSDC, meaning it aligns with industry standards and has a good chance of leading to employment.
- The training center has met certain quality standards, such as having a certain number of trainers and equipment.
- You may be eligible for a loan from the NSDC to pay for the course.
- You may be able to get a certificate recognized by the government and industry.
- You may be able to get placement assistance after completing the course
Instructor
Pass Percentage
