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

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.

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## 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](https://jamescook.dev/azure-dp900-storyandstudyresources)), 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](https://www.microsoft.com/en-gb/events/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.

* [Get started with artificial intelligence on Azure](https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/)

* [Create no-code predictive models with Azure Machine Learning](https://docs.microsoft.com/en-us/learn/paths/create-no-code-predictive-models-azure-machine-learning/)

* [Explore computer vision in Microsoft Azure](https://docs.microsoft.com/en-us/learn/paths/explore-computer-vision-microsoft-azure/)

* [Explore natural language processing](https://docs.microsoft.com/en-us/learn/paths/explore-natural-language-processing/)

* [Explore conversational AI](https://docs.microsoft.com/en-us/learn/paths/explore-conversational-ai/)

## 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. Identify Azure tools and services for computer vision tasks

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. Identify Azure tools and services for NLP workloads

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.

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