- Describe Artificial Intelligence Workloads and Considerations (15–20%)
- Describe Fundamental Principles of Machine Learning on Azure (20–25%)
- 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 Generative AI Workloads on Azure (15–20%)
AI workloads refer to tasks and processes that involve the application of artificial intelligence techniques. Common AI workloads include:
- Machine Learning: Training models on data to make predictions or classify data.
- Azure Tool: Azure Machine Learning
- Computer Vision: Analyzing visual data to identify objects, faces, or scenes.
- Azure Tool: Azure Computer Vision
- Natural Language Processing (NLP): Understanding and generating human language.
- Azure Tool: Azure Cognitive Services for Language
- Generative AI: Creating new content, such as images or text, using AI models.
- Azure Tool: Azure OpenAI Service
- Content Moderation: AI models that automatically detect and filter inappropriate content.
- Azure Tool: Azure Content Moderator
- Personalization: Tailoring content and recommendations to individual users based on their preferences and behavior.
- Azure Tool: Azure Personalizer
Computer vision involves interpreting and processing visual data. Workloads include:
- Image Classification: Assigning labels to images.
- Object Detection: Identifying and locating objects within an image.
- Optical Character Recognition (OCR): Converting images of text into machine-readable text.
- Facial Detection and Analysis: Identifying and analyzing human faces in images or videos.
- Azure Tool: Azure Computer Vision, Azure Face
NLP deals with the interaction between computers and human language. Common workloads include:
- Key Phrase Extraction: Identifying important phrases in text.
- Entity Recognition: Detecting and classifying entities such as names, dates, and locations in text.
- Sentiment Analysis: Determining the sentiment or emotion expressed in text.
- Language Modeling: Predicting the next word or phrase in a sequence.
- Speech Recognition and Synthesis: Converting speech to text and vice versa.
- Translation: Translating text or speech from one language to another.
Knowledge mining involves extracting useful information from large datasets, often using AI and machine learning techniques.
- Azure Tool: Azure Cognitive Search
Document intelligence includes extracting, processing, and understanding information from documents, such as forms and invoices.
- Azure Tool: Azure Form Recognizer
Generative AI involves creating new content using AI models, such as:
- Text Generation: Creating human-like text based on input prompts.
- Image Generation: Creating images from descriptions or other inputs.
- Azure Tool: Azure OpenAI Service
Responsible AI principles ensure that AI is used ethically and safely. Key considerations include:
- Fairness: Avoiding bias in AI solutions.
- Reliability and Safety: Ensuring AI solutions work reliably and do not cause harm.
- Privacy and Security: Protecting user data and ensuring secure AI systems.
- Inclusiveness: Making AI accessible to all users.
- Transparency: Being open about how AI systems work and make decisions.
- Accountability: Ensuring there is accountability for AI-driven decisions and actions.
- Azure Resource: Microsoft Responsible AI
Machine learning techniques include:
- Regression: Predicting continuous values.
- Classification: Assigning labels to data points.
- Clustering: Grouping similar data points together.
- Deep Learning: Using neural networks with many layers for complex tasks.
- Azure Tool: Azure Machine Learning
- Regression: Predicting house prices based on features like size and location.
- Classification: Email spam detection.
- Clustering: Customer segmentation in marketing.
Deep learning involves neural networks with multiple layers (deep neural networks) to model complex patterns in data, useful for tasks like image and speech recognition.
- Azure Tool: Azure Machine Learning
Core concepts include:
- Features and Labels: Features are input variables, and labels are the output we predict.
- Training and Validation Datasets: Training data is used to train the model, and validation data is used to evaluate its performance.
- Azure Tool: Azure Machine Learning
Azure Machine Learning provides tools and services for:
- Automated Machine Learning (AutoML): Automating the process of training and tuning models.
- Data and Compute Services: Managing data and computational resources for machine learning.
- Model Management and Deployment: Managing the lifecycle of machine learning models from training to deployment.
- Azure Tool: Azure Machine Learning
- Image Classification: Assigning labels to images.
- Object Detection: Identifying and locating multiple objects within an image.
- Optical Character Recognition (OCR): Extracting text from images.
- Facial Detection and Analysis: Recognizing and analyzing faces.
- Azure Tool: Azure Computer Vision, Azure Face
Identify Features of Image Classification, Object Detection, Optical Character Recognition, and Facial Detection/Analysis Solutions
- Image Classification: Assigning categories to images.
- Object Detection: Identifying and locating multiple objects within an image.
- Optical Character Recognition (OCR): Converting images of text into machine-readable text.
- Facial Detection/Analysis: Detecting faces and analyzing facial features and expressions.
- Azure Tool: Azure Computer Vision, Azure Face
- Azure AI Vision Service: Provides APIs for image analysis, OCR, and more.
- Azure Tool: Azure Computer Vision
- Azure AI Face Service: Provides facial recognition and analysis capabilities.
- Azure Tool: Azure Face
- Azure AI Video Indexer: Analyzes videos to extract insights.
- Azure Tool: Azure Video Indexer
- Key Phrase Extraction: Identifying important phrases in text.
- Entity Recognition: Detecting and classifying entities in text.
- Sentiment Analysis: Determining the sentiment expressed in text.
- Language Modeling: Predicting the next word or phrase.
- Speech Recognition and Synthesis: Converting speech to text and vice versa.
- Translation: Translating text or speech between languages.
Identify Features and Uses for Key Phrase Extraction, Entity Recognition, Sentiment Analysis, Language Modeling, Speech Recognition and Synthesis, and Translation
- Key Phrase Extraction: Summarizing the main points in a text.
- Entity Recognition: Identifying entities such as names and dates.
- Sentiment Analysis: Measuring sentiment in customer feedback.
- Language Modeling: Autocompleting sentences.
- Speech Recognition and Synthesis: Voice assistants and transcription services.
- Translation: Real-time translation services.
- Azure AI Language Service: Provides NLP capabilities like text analytics and language understanding.
- Azure Tool: Azure Cognitive Services for Language
- Azure AI Speech Service: Offers speech-to-text, text-to-speech, and translation services.
- Azure Tool: Azure Speech
- Azure AI Translator Service: Translates text and speech between languages.
- Azure Tool: Azure Translator
Generative AI models can create new content, such as text, images, and code. They include:
- GPT Models: Generate human-like text.
- DALL-E Models: Create images from textual descriptions.
- Azure Tool: Azure OpenAI Service
- Content Creation: Writing articles or generating creative content.
- Image Creation: Generating images for art and design.
- Code Generation: Assisting in programming by generating code snippets.
- Azure Tool: Azure OpenAI Service
- Ethical Use: Ensuring AI-generated content is used ethically.
- Bias Mitigation: Addressing biases in generated content.
- Transparency: Clearly indicating when content is AI-generated.
- Azure Resource: Microsoft Responsible AI
- Natural Language Generation: Generating text based on prompts.
- Code Generation: Assisting developers by generating code.
- Image Generation: Creating images from text descriptions.
- Azure Tool: Azure OpenAI Service
| Resource | Description |
|---|---|
| Review the skills measured as of April 24, 2024 | Study this list if you plan to take the exam after this date. |
| Review the skills measured prior to April 24, 2024 | Study this list if you plan to take the exam before this date. |
| Exam sandbox | Explore the exam environment. |
| Request accommodations | Request exam accommodations if needed. |
| Free Practice Assessment | Test your skills with practice questions. |
| Microsoft Learn Profile | Connect your certification profile to Microsoft Learn. |
| Certification Renewal | Renew your certifications annually by passing a free online assessment. |
| Resource | Link |
|---|---|
| Azure Machine Learning | Comprehensive documentation on Azure Machine Learning. |
| Computer Vision | Detailed guides and tutorials on Azure Computer Vision. |
| Azure AI Language | Documentation for Azure AI Language services. |
| Azure AI Speech | Information on Azure AI Speech services. |
| Azure Bot Service | Guides and tutorials for Azure Bot Service. |
| Azure OpenAI Service | Documentation on Azure OpenAI Service. |
For detailed changes in the exam skills measured, refer to the Change Log.
This cheat sheet provides an overview of the key topics for the AI-900 exam. Use the provided links and resources to deepen your understanding and prepare effectively.
Good luck with your exam!