Azure AI Fundamentals (AI-900) - My Story and Study Resources

Azure AI Fundamentals (AI-900) - My Story and Study Resources

Reviewed May 2021

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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

1.1.2. identify features of anomaly detection workloads

1.1.3. identify computer vision workloads

1.1.4. identify natural language processing or knowledge mining workloads

1.1.5. identify conversational AI workloads

1.2. Identify guiding principles for responsible AI

1.2.1. describe considerations for fairness in an AI solution

1.2.2. describe considerations for reliability and safety in an AI solution

1.2.3. describe considerations for privacy and security in an AI solution

1.2.4. describe considerations for inclusiveness in an AI solution

1.2.5. describe considerations for transparency in an AI solution

1.2.6. describe considerations for accountability in an AI solution

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

2.1.2. identify classification machine learning scenarios

2.1.3. identify clustering machine learning scenarios

2.2. Describe core machine learning concepts

2.2.1. identify features and labels in a dataset for machine learning

2.2.2. describe how training and validation datasets are used in machine learning

2.2.3. describe how machine learning algorithms are used for model training

2.2.4. select and interpret model evaluation metrics for classification and regression

2.3. Identify core tasks in creating a machine learning solution

2.3.1. describe common features of data ingestion and preparation

2.3.2. describe feature engineering and selection

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

2.4. Describe capabilities of no-code machine learning with Azure Machine Learning studio

2.4.1. automated ML UI

2.4.2. azure Machine Learning designer

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

3.1.2. identify features of object detection

3.1.3. identify features of optical character recognition solutions

3.1.4. identify features of facial detection, facial recognition, and facial analysis solutions

3.2. Identify Azure tools and services for computer vision tasks

3.2.1. identify capabilities of the Computer Vision service

3.2.2. identify capabilities of the Custom Vision service

3.2.3. identify capabilities of the Face service

3.2.4. identify capabilities of the Form Recognizer service

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

4.1.2. identify features and uses for entity recognition

4.1.3. identify features and uses for sentiment analysis

4.1.4. identify features and uses for language modeling

4.1.5. identify features and uses for speech recognition and synthesis

4.1.6. identify features and uses for translation

4.2. Identify Azure tools and services for NLP workloads

4.2.1. identify capabilities of the Text Analytics service

4.2.2. identify capabilities of the Language Understanding service (LUIS)

4.2.3. identify capabilities of the Speech service

4.2.4. identify capabilities of the Translator Text service

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

5.1.2. identify common characteristics of conversational AI solutions

5.2. Identify Azure services for conversational AI

5.2.1. identify capabilities of the QnA Maker service

5.2.2. identify capabilities of the Azure Bot service

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.

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