Microsoft AI-900 Exam Practice Questions

Explore the world of Artificial Intelligence (AI) with this comprehensive course! You'll gain a deep understanding of various AI workloads and how to implement them effectively.

AI Workloads & Considerations:

• Understanding AI Workloads: Learn to describe different types of AI workloads and the key considerations for their successful deployment.
• Data Monitoring & Anomaly Detection: Identify features and best practices for data monitoring and detecting anomalies within your datasets.
• Content Moderation & Personalization: Discover how AI powers content moderation and personalized experiences.
• Computer Vision: Delve into computer vision workloads, including image classification, object detection, and facial analysis.
• Natural Language Processing (NLP): Master NLP techniques for tasks like key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition, translation, and more.
• Knowledge Mining: Learn how to extract valuable insights from large datasets using knowledge mining techniques.
• Document Intelligence: Explore how AI can be used to extract information and insights from documents.
• Generative AI: Understand the capabilities and responsible use of generative AI models.

Responsible AI & Ethical Considerations:

This course emphasizes building AI solutions responsibly. You'll learn about:

• Guiding Principles for Responsible AI: A framework for ethical AI development.
• Fairness in AI: Considerations for ensuring fairness and mitigating bias in AI solutions.
• Reliability & Safety: Strategies for ensuring the reliability and safety of AI systems.
• Privacy & Security: Best practices for protecting data privacy and security in AI applications.
• Inclusiveness: Designing AI solutions that are inclusive and accessible to all.
• Transparency: Promoting transparency in AI decision-making processes.
• Accountability: Establishing accountability for AI system outcomes.

Fundamental Machine Learning on Azure:

• Core Machine Learning Concepts: Grasp fundamental machine learning principles.
• Machine Learning Techniques: Identify and apply common machine learning techniques.
• Regression, Classification & Clustering: Understand and apply regression, classification, and clustering machine learning scenarios.
• Deep Learning: Explore the features of deep learning techniques.
• Data Preparation: Learn how to identify features and labels in datasets for machine learning.
• Training & Validation: Understand the roles of training and validation datasets in the machine learning process.
• Azure Machine Learning: Explore the capabilities of Azure Machine Learning for building, training, and deploying models.
• Automated Machine Learning (AutoML): Discover the power of AutoML to automate model building.
• Data & Compute Services: Utilize Azure's data and compute services for data science and machine learning.
• Model Management & Deployment: Learn how to manage and deploy models effectively in Azure Machine Learning.

Computer Vision on Azure:

• Computer Vision Solutions: Identify common types of computer vision solutions.
• Image Classification: Implement image classification solutions.
• Object Detection: Develop object detection solutions.
• Optical Character Recognition (OCR): Utilize OCR for extracting text from images.
• Facial Detection & Analysis: Implement facial detection and facial analysis solutions.
• Azure AI Vision Service: Leverage the capabilities of the Azure AI Vision service.
• Azure AI Face Service: Utilize the Azure AI Face detection service.
• Azure AI Video Indexer: Explore the capabilities of the Azure AI Video Indexer service.

Natural Language Processing (NLP) on Azure:

• NLP Workload Scenarios: Identify features of common NLP workload scenarios.
• Key Phrase Extraction: Extract key phrases from text.
• Entity Recognition: Identify and extract entities from text.
• Sentiment Analysis: Analyze the sentiment expressed in text.
• Language Modeling: Utilize language modeling techniques.
• Speech Recognition & Synthesis: Implement speech recognition and synthesis solutions.
• Translation: Leverage translation capabilities.
• Azure AI Language Service: Utilize the capabilities of the Azure AI Language service.
• Azure AI Speech Service: Leverage the capabilities of the Azure AI Speech service.
• Azure AI Translator Service: Utilize the capabilities of the Azure AI Translator service.

Generative AI on Azure:

• Generative AI Solutions: Identify features of generative AI solutions.
• Generative AI Models: Understand the features of generative AI models.
• Common Generative AI Scenarios: Explore common use cases for generative AI.
• Azure OpenAI Service: Leverage the power of the Azure OpenAI Service.