I passed the Microsoft Azure AI Fundamentals exam at the beginning of May 2021. To give back to the community, I decided to share my experience and the study resources I used to prepare for the exam.
First, why did I take the exam 🤷♂️
I understand AI is used in many of the devices I use, as well as some of the web services I utilise. I was interested what I could learn about AI and what I could use as an Azure user, so this brought me to the course material and exam. I felt if I was to learn the basics for this, I could come out with a certification and maybe apply this new knowledge at home or at work.
Microsoft Virtual Training Days 👩🏫
After experiencing a Microsoft Virtual Training day for my DP-900 exam (story and resources here), I felt the same approach by doing one of these would benefit me to start understanding AI and Azure services. I visited the Virtual Training Days site and found a date I could attend the video course. After completing the training days, I received a free exam voucher for the AI-900 exam.
Microsoft Learn 🏙️
I found the Virtual Training Day covers all areas in AI but felt I needed to dig a little further into each service. This brings me to Microsoft Learn where I completed each of the following modules.
Was the above enough ⚖️
I do believe both Virtual Training Day and the Microsoft Learn content is enough to understand AI and Azure services for AI. I did had to refer to some of the documentation below but this was out of interest in learning more about some of the services. However, I did go out looking for more information on Microsoft's websites to provide to you for your studies if necessary.
1️⃣ Describe Artificial Intelligence workloads and considerations (15-20%)
1.1. Identify features of common AI workloads
1.1.1. identify prediction/forecasting workloads
- https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/demand-forecasting
1.1.2. identify features of anomaly detection workloads
- https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/anomaly-detector-process
1.1.3. identify computer vision workloads
- https://azure.microsoft.com/en-gb/services/cognitive-services/computer-vision/
1.1.4. identify natural language processing or knowledge mining workloads
- https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
1.1.5. identify conversational AI workloads
- https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot
1.2. Identify guiding principles for responsible AI
1.2.1. describe considerations for fairness in an AI solution
- https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml
1.2.2. describe considerations for reliability and safety in an AI solution
- https://azure.microsoft.com/en-gb/features/reliability/
1.2.3. describe considerations for privacy and security in an AI solution
- https://azure.microsoft.com/en-gb/support/legal/cognitive-services-compliance-and-privacy/
1.2.4. describe considerations for inclusiveness in an AI solution
- https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6
1.2.5. describe considerations for transparency in an AI solution
- https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
1.2.6. describe considerations for accountability in an AI solution
- https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
2️⃣ Describe fundamental principles of machine learning on Azure (30-35%)
2.1. Identify common machine learning types
2.1.1. identify regression machine learning scenarios
- https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml#regression
2.1.2. identify classification machine learning scenarios
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-select-algorithms
2.1.3. identify clustering machine learning scenarios
- https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/k-means-clustering
2.2. Describe core machine learning concepts
2.2.1. identify features and labels in a dataset for machine learning
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-labeling-projects
2.2.2. describe how training and validation datasets are used in machine learning
- https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
2.2.3. describe how machine learning algorithms are used for model training
- https://azure.microsoft.com/en-gb/overview/machine-learning-algorithms/
2.2.4. select and interpret model evaluation metrics for classification and regression
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
2.3. Identify core tasks in creating a machine learning solution
2.3.1. describe common features of data ingestion and preparation
- https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion
2.3.2. describe feature engineering and selection
- https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features
2.3.3. describe common features of model training and evaluation
- Refer to MS Learn content
2.3.4. describe common features of model deployment and management
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where?tabs=azcli
2.4. Describe capabilities of no-code machine learning with Azure Machine Learning studio
2.4.1. automated ML UI
- https://azure.microsoft.com/en-gb/blog/simplifying-ai-with-automated-ml-no-code-web-interface/
2.4.2. azure Machine Learning designer
- https://azure.microsoft.com/es-es/blog/simplifying-ai-with-automated-ml-no-code-web-interface/
3️⃣ Describe features of computer vision workloads on Azure (15-20%)
3.1. Identify common types of computer vision solution
3.1.1. identify features of image classification solutions
- https://docs.microsoft.com/en-us/azure/architecture/example-scenario/ai/intelligent-apps-image-processing
3.1.2. identify features of object detection
- https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
3.1.3. identify features of optical character recognition solutions
- https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
3.1.4. identify features of facial detection, facial recognition, and facial analysis solutions
- https://azure.microsoft.com/en-gb/services/cognitive-services/face/
3.2.1. identify capabilities of the Computer Vision service
- https://azure.microsoft.com/en-gb/services/cognitive-services/computer-vision/
3.2.2. identify capabilities of the Custom Vision service
- https://azure.microsoft.com/en-gb/services/cognitive-services/custom-vision-service/
3.2.3. identify capabilities of the Face service
- https://azure.microsoft.com/en-gb/services/cognitive-services/face/
3.2.4. identify capabilities of the Form Recognizer service
- https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/
4️⃣ Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
4.1. Identify features of common NLP Workload Scenarios
4.1.1. identify features and uses for key phrase extraction
- https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-keyword-extraction
4.1.2. identify features and uses for entity recognition
- https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview
4.1.3. identify features and uses for sentiment analysis
- https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis?tabs=version-3-1
4.1.4. identify features and uses for language modeling
- https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-language-detection
4.1.5. identify features and uses for speech recognition and synthesis
- https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text/
4.1.6. identify features and uses for translation
- https://azure.microsoft.com/en-gb/services/cognitive-services/translator/
4.2.1. identify capabilities of the Text Analytics service
- https://azure.microsoft.com/en-gb/services/cognitive-services/text-analytics/
4.2.2. identify capabilities of the Language Understanding service (LUIS)
- https://azure.microsoft.com/en-gb/services/cognitive-services/language-understanding-intelligent-service/
4.2.3. identify capabilities of the Speech service
- https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/
4.2.4. identify capabilities of the Translator Text service
- https://docs.microsoft.com/en-us/azure/cognitive-services/translator/translator-info-overview
5️⃣ Describe features of conversational AI workloads on Azure (15-20%)
5.1. Identify common use cases for conversational AI
5.1.1. identify features and uses for webchat bots
- https://azure.microsoft.com/en-gb/services/bot-services/
5.1.2. identify common characteristics of conversational AI solutions
- https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/conversational-ai
5.2. Identify Azure services for conversational AI
5.2.1. identify capabilities of the QnA Maker service
- https://azure.microsoft.com/en-gb/services/cognitive-services/qna-maker/
5.2.2. identify capabilities of the Azure Bot service
- https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/conversational-ai
Conclusion ✍️
With an understanding of the AI services by Microsoft Azure, I have been able to relate some of this to projects at home or work. Even though the course was fundamental information, I learnt how to apply this knowledge in services, improving my ability to advice and plan on utilising AI. I can now look at the possibility of doing more with AI and dig more into training and more certifications on the topic.
