I’ve just completed the Microsoft Certified: Azure AI Fundamentals exam. I thought it’d be useful to go into what you should study and understand before sitting the exam, keep in mind I can’t share the exam details, but I can give you a general guide based off of Microsoft’s study guide.
🎯 Who Should Consider This Certification?
Whether you’re an AI Engineer, Developer, Data Scientist, Student, or an enthusiast from a non-technical background, this certification is your gateway to exploring and validating your knowledge in AI and ML.
📘 Key Areas to Explore
Understanding AI Workloads and Considerations (15–20%)
Identify Features of Common AI Workloads
- Data Monitoring and Anomaly Detection: Understand how to identify unusual patterns or activities using Azure’s Anomaly Detector API.
- Content Moderation and Personalization: Learn how to filter and control accessible content and personalize user experiences.
- Computer Vision: Explore image classification, object detection, and OCR.
- Natural Language Processing: Dive into key phrase extraction, entity recognition, and sentiment analysis.
- Knowledge Mining: Understand how to extract actionable insights from structured and unstructured content.
- Document Intelligence: Learn how to analyze and extract information from documents.
- Generative AI: Explore generative AI models and their common scenarios.
Guiding Principles for Responsible AI
- Understand and apply considerations for fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability in AI solutions.
Fundamental Principles of Machine Learning on Azure (20–25%)
Identify Common Machine Learning Techniques
- Understand scenarios and applications for regression, classification, clustering, and deep learning techniques.
Core Machine Learning Concepts
- Learn about features and labels in datasets and the usage of training and validation datasets.
Azure Machine Learning Capabilities
- Explore Automated ML, data and compute services, and model management and deployment capabilities in Azure Machine Learning.
Features of Computer Vision Workloads on Azure (15–20%)
Identify Common Types of Computer Vision Solution
- Understand image classification, object detection, OCR, and facial detection and analysis solutions.
Azure Tools and Services for Computer Vision Tasks
- Explore the capabilities of Azure AI Vision service, Azure AI Face detection service, and Azure AI Video Indexer service.
Features of Natural Language Processing (NLP) Workloads on Azure (15–20%)
Identify Features of Common NLP Workload Scenarios
- Understand and implement key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition and synthesis, and translation.
Azure Tools and Services for NLP Workloads
- Learn about the capabilities of Azure AI Language service, Azure AI Speech service, and Azure AI Translator service.
Features of Generative AI Workloads on Azure (15–20%)
Identify Features of Generative AI Solutions
- Understand generative AI models, common scenarios, and responsible AI considerations for generative AI.
Capabilities of Azure OpenAI Service
- Explore natural language generation, code generation, and image generation capabilities of Azure OpenAI Service.
🔍 Example of Deep Diving into Specific Concepts
- Data Management: Learn the significance of data splitting for effective model training and evaluation.
- Anomaly Detection: Utilize Azure’s Anomaly Detector API to identify unusual patterns in data over time.
- Generative AI: Explore generative AI solutions and understand the capabilities of Azure OpenAI Service.
📚 Preparation Path
- Utilize the study guide and resources provided by Microsoft.
- Engage with the community, ask questions, and share knowledge.
- Take practice assessments to validate your preparation.
🎉 Benefits of Certification
- Showcase your skills and knowledge in AI and ML.
- Utilize the certification as a stepping stone for your career.
- Celebrate your achievement by sharing your certification badge on LinkedIn.
🔗 Useful Links
About the Author:
As a highly skilled and accomplished cloud architect and consultant, I bring over 15 years of experience to the table. Throughout my career, I have demonstrated a strong strategic mindset and technical proficiency in designing, implementing, and managing IT systems for large organizations.
My expertise in cloud architecture is matched only by my exceptional communication and stakeholder engagement skills, making me a trusted advisor in both corporate and government environments. I have a proven track record of successfully aligning digital strategy with business objectives, resulting in improved efficiencies, cost savings, and successful transformation projects that drive growth and innovation.
I am passionate about refining development practices and processes to consistently deliver top-tier solutions that meet the unique needs of each organization I work with. My commitment to staying current on the latest cloud technologies and industry best practices ensures that I can always provide expert guidance and support to my clients.
With my extensive experience in cloud architecture and consulting, I am confident in my ability to help organizations leverage the full potential of cloud technology to achieve their goals and drive success. Whether it’s designing and implementing cloud-based solutions or providing expert advice and guidance, I am committed to delivering outstanding results that exceed expectations.
McAlpine, L. (2023). Microsoft Certified: Azure AI Fundamentals – Study Prep. Available at: https://www.linkedin.com/pulse/microsoft-certified-azure-ai-fundamentals-study-prep-mcalpine/ [Accessed: 16th January 2024].
