A Complete Guide To Microsoft Certified - Azure AI Fundamentals (Exam AI-900)

This article is a guide for the preparation of Exam AI – 900 to achieve the Microsoft Certification in Azure AI Fundamentals. Artificial Intelligence has the potential to drive humanity forward in an exponential impact index that hasn’t surfaced yet. The untapped potential of AI will take years, if not many more decades to come to fruition before its growth comes to a halt. The field of Artificial Intelligence is booming and so is the need for engineers who can contribute to this wonderful area. The Career Prospect in AI is huge and Certifications help individuals stand out from the rest competitor during job application or even if one is looking to switch roles or grow in their prospective company itself. Microsoft Certified: Azure AI Fundamentals is one such certification which is a stepping stone to the field of AI which focuses in Azure Services dedicated for making AI enabled products.

Topics covered in this article for Exam AI-900 

• Brief Introduction to Microsoft Azure AI Fundamentals Certification and AI – 900 Exam 

• Passing Score 

• Exam Scheduling 

• Proctor 

• How to Prepare for the AI – 900 Exam?

1. Basics of AI on Azure (15-20%)

   1. Artificial Intelligence 

   2. Machine Learning 

   3. Microsoft AI 

   4. Anomaly Detection 

2. Azure Machine Learning (30-35%)

   1. Azure Machine Learning Studio 

   2. Machine Learning Models in Azure 

   3. Azure Machine Learning Designer 

   4. Regression Model 

   5. Classification Model 

   6. Clustering Model 

   7. Automated Machine Learning

   8. Azure Cognitive Services 

3. Computer Vision (15-20%)

   1. Face Detection 

   2. Face Recognition 

   3. Custom Vision 

   4. Object Detection 

   5. Optical Character Recognition 

   6. Form Recognizer 

4. Natural Language Processing (15-20%)

   1. Speech 

   2. Language Understanding Intelligent Service (LUIS) 

   3. Text Analytics 

   4. Translator Text 

5. Conversational AI (15-20%)

   1. QnA Maker 

   2. Azure Bot Service 

6. Responsible AI (5-10%)

   1. Fairness 

   2. Reliability and Safety 

   3. Privacy and Security 

   4. Inclusiveness 

   5. Transparency 

   6. Accountability 

• Model Questions for AI – 900 Exam (12 Questions) 

• Answers with Illustrated Explanation 

What is Microsoft Certified: Azure AI Fundamentals and Exam AI - 900? 

The Exam AI – 900: Microsoft Azure AI Fundamentals tests the foundational knowledge of artificial intelligence, machine learning and services in Microsoft Azure that are catered to AI and ML to solve complex problems. With Microsoft Azure catering to over 19% of the market share, it is one of the most sought-after cloud ecosystems and has huge potentials for technology enthusiasts and seasoned professionals. Henceforth, Microsoft has different certification programs to showcase and register your expertise in comparison to what the market demands the workforce and with Exam AI – 900, Microsoft honors the candidates with Microsoft Certification on Azure AI Fundamentals as one passes the exam.

Passing Score

The Passing Score for the Test is 700 and takes an estimated time of 80 Minutes. The main exam is 60 minutes long with 30 minutes prior check-in from the appointed allotted time.

Exam Scheduling

The Exam can be scheduled from Microsoft Learn Certification Section. Just Choose the Certification you want to receive, Schedule Your Date, Pay the Exam Fee and you are good to go. The rescheduling can be done 24-hour prior to exam.

Proctor

The Exam can be taken in Microsoft Test Centers or at the comfort at your home/ office. While taking the exam at home, you’ll be monitored via Webcam and Audio. Your room will be administered from a call from Proctor during the check-in before the exam. A more detailed article regarding Do’s and Don'ts during the exam will be shared in the upcoming days.

How to Prepare for the AI – 900 Exam?

The best way to learn is to get hands-on experience. Microsoft Learn is a great platform to get the resources which will enable one to get engaged with the Azure Services dedicated to AI. If you have a decent amount of time, it would be wise to explore those. This article however in other hand, will give you a preview of the learnings needed and help you get used to with the types of questions that will be there during the exam.

In this article, we’ll go in brief about the different Azure Services dedicated for AI and then explore the various modeled questions.

1. Basics of AI on Azure 

This exam is dedicated to judge your understanding of AI and how Azure comes into play. If you are an AI Engineer, you’ll be using these very tools to produce AI enabled applications. The first step is to learn what Artificial Intelligence and Machine Learning is and how Microsoft AI caters to this area.

Artificial Intelligence (AI) 

Artificial Intelligence (AI) is the branch of computer science with multiple inter-relations to various domains which refers to the creation of intelligence forms that imitate human capabilities and behavior. Artificial intelligence was first ever coined in 1955 and was envisioned for general artificial intelligence during the initial inception but later, progressed into domain-specific and task-based artificial intelligence. 

To Learn more about Artificial Intelligence, Check out the previous article, Artificial Intelligence Overview 

Machine Learning (ML) 

Machine Learning refers to the process by which machines can be taught to learn from data. It is the approach of teaching computer models to learn from data to make predictions and draw out conclusions. Usually, huge data is needed to create these models and to train them to design an effective and accurate system. Machine Learning is a subject of AI and it is an approach to solve numerous problems. From Computer Vision to Natural Language Processing to Analysis in Stock Market and Healthcare, Machine Learning is everywhere. It is tremendously powerful and has a subset under it to solve even exponentially difficult problems. 

Microsoft AI

Microsoft AI is a powerful framework that enables organizations, researchers, and non-profits to use AI technologies with its powerful framework which offers services and features across domains of Machine Learning, Robotics, Data Science, IoT, and many more. Learn more about Microsoft AI from the previous article, Microsoft Azure AI Fundamentals. Some key features in Azure for AI are as follows.

• Cognitive Services 
  The tools are based on the human sense, for instance, computer vision tries to mimic and copy the functionality of the human eye. Similarly, different listening capabilities of humans are made a reality through Natural Language Processing. Smart devices today able to listen and translate sound such as – the Intelligent bot services. 

• Personalized Models 
  The developers are enabled to make their own models without using the rest APIs. Azure Machine Learning Services provides automated machine learning model services. 

• Drag and Drop 
  The Drap and Drop functionality is accessed with the Designer in Azure. You can easily build an intelligent system with just drag and drop features. Inside this, we can use a notebook or import your code in your favorite IDE to execute our commands. 

Anomaly Detection 

Anomaly detection helps in finding out anomaly ie. Usually detection of errors and unusual activities. Anomaly basically means something beyond normal. It is commonly used in the field of Data Mining. Machine Learning can be implemented to detect cases of an anomaly in multitudes of scenarios through supervised and unsupervised methods. It is vigorously used to detect errors and failures, thus help to prevent crisis scenarios. 

2. Azure Machine Learning 

Azure Machine Learning Studio 

The Azure Machine Learning Studio is mostly dedicated to developers and data scientists which provide a graphical user interface-based platform to construct and use the workflows to solve Machine Learning problems through Azure services. 

Machine Learning Models in Azure 

Azure has a Machine Learning Studio which can perform all necessary tasks for production-grade Machine Learning from Data selection to Model Building and analysis for higher accuracy of the system. 

The Model is trained, scored, and evaluated and with the experimental results, different algorithms are used to provide better results with re-iterated operations. 

Moreover, Regression, Classification and Clustering Models can be created with Azure Machine Learning Designer. 

Azure Machine Learning Designer 

Azure Machine Learning Designer enables training and deployment of machine learning models with Drag and Drop feature. The Machine Learning Pipelines can be created using datasets and models with just Drag and Drop without the need of having to write any code.

 
What kinds of models can Azure Machine Learning Studio help create? 

Regression Model

Regression is a statistical approach to estimate relationship between a dependent variable and one a more independent variable. It is a supervised learning technique and can be used for the prediction of continuous value such as house price prediction.

Classification Model

Classification Model is also a supervised machine learning technique which focuses on predicting classes and categories from inputted data. From Spam Detection, Dog Breed Detection to Sentiment Analysis, Classification Model plays a huge role.

Clustering Model

Clustering falls under unsupervised machine learning which focuses on grouping entities that are similar to each other based on feature. Algorithms such a K-Means can be used to cluster images based on colors.

Azure Machine Learning Designer enables to create all the above models with ease, just with a few Drag and Drop. This enables even people without sound background in Machine Learning to create ML Models and experiment with the capabilities of Artificial Intelligence. Azure also supports, Automated Machine Learning ie. Auto ML. 

Automated Machine Learning 

Automated Machine Learning (Auto ML) refers to automating the machine learning model development process which is mostly iterative and extremely time-consuming which enables developers, analysts, and data scientists to build highly scalable, efficient, and productive Machine Learning Models. Azure provides the feature of Auto ML which makes it easier to obtain production-ready Machine Learning Models without having to spend much time. Dozens of Models can be created and compared at the same time with the accurate ones to be decided for usage. 

To learn Automated Machine Learning, it would be easier if you experience hands-on learning to create resources for Machine Learning in Azure itself. You can learn on creating Cognitive Services from the previous article, How to Create a Cognitive Service in Azure which is dedicated to it.

Azure Cognitive Services 

Azure Cognitive Services enables organizations to build cognitive intelligence for applications with client library SDKs and REST APIs. Azure Cognitive Services allows developers to integrate cognitive features into applications with no prior knowledge of Machine Learning, Data Science, and Artificial Intelligence skillset. From Computer Vision to Natural Language Processing and Conversational AI, Azure Cognitive Services enables all kinds of applications.

Microsoft Azure provides various services that caters to different areas of AI including Machine Learning, Anomaly Detection, Computer Vision, Natural Language Processing, Conversational AI with focusing about Responsible AI in practice.

3. Computer vision 

Computer Vision is synonymous with its name. This branch of AI aids by supporting computers to analyze data from images, cameras, videos, and other visual content. Check out object detection implemented with a hands-on example on this previous article Object Detection with ImageAI. Computer Vision mainly deals with the processing of visual data such as images or videos. Multitudes of Machine Learning Models can be implemented using various Algorithms to perform different tasks. Some of the key tasks are performed in Computer Vision are listed below. 

Face Detection 

Face detection as the name suggests in a technology using which human faces can be detected in images, videos, and other digital forms. This has huge implications for systems from airports to shopping malls. Its applications and usage are only limited to human imagination.

Face Recognition 

Face Recognition is the capability of a system to recognize and distinguish a specific person through matching the digital image or video from the database of faces. From usage for security to unlock phones to identify people in social media platforms like Facebook, face recognition has huge implications.

To learn more with hands-on experience with Azure Cognitive Services, Check this previous article, Face Detection and Recognition with Azure Cognitive Services. 

Custom Vision

Custom Vision in Microsoft Azure enables the training of custom image classification and object detection models with own images. 

Object Detection 

Object Detection is performed by training models to classify individual objects within the frame of the image or video. With object detection, we can detect various images within an image, for what the model is trained for. 

Optical Character Recognition 

Optical Character Recognition (OCR) is a technique that enables the reading and detection of text. It can be used to read characters from images, photographs, scanned documents, currency, and more. 

Form Recognizer

With Form Recognizer Service in Microsoft Azure, information can be extracted from the scanned invoices and forms. This feature would help create applications to extract data with intelligent data capture eliminating manual entry minimizing human errors.

4. Natural Language Processing 

Natural Language Processing (NLP) refers to the ability of computers to understand the natural languages of humans through audio or text means. Microsoft’s Cortana Assistant is an example of NLP. Microsoft Azure supports to build Natural Language Processing solutions with its Various cognitive services. They are as listed below.

Speech 

The Speech service of Microsoft Azure enables developers to create applications with the feature to synthesize and recognize speech and to translate verbal languages. 

Language Understanding Intelligent Service (LUIS) 

Text-based commands and spoken language can be trained using the LUIS feature provided by Microsoft Azure. 

Text Analytics 

Text Analysis help to analyze text documents and to extract required key phrases, perform sentiment analysis in term of positive of negative and supports the detection of entities, for instance, location, people and date. 

Translator Text 

The Translator Text feature provided by Microsoft Azure helps translate text in over sixty different languages. 

5. Conversational AI 

Conversational AI refers to the ability of computers to engage and participate in natural conversation in spoken or written language. Conversational AI can be understood as this specific type of AI which is capable of having a to and fro conversation with a human entity. It is prevalent in social media messaging platforms, phone calls, and web interfaces to use this technology. 

QnA Maker 

QnA Maker is a cognitive service provided by Microsoft AI to build a knowledge-based question-answer system, which makes it capable for the AI agent to have a decent conversation with the human agent. 

Azure Bot Service 

Azure Bot Service enables developers with the bot framework to create, publish and manage bots service such that back end services such as LUIS and QnA Maker can be integrated into the system and be connected to various channels such as emails, Microsoft Teams, and web chats. 

6. Responsible AI 

Artificial Intelligence has the potential to create a huge impact in almost every field of society. It can transform industries just like electricity. From manufacturing, security, communication, healthcare, agriculture – it can touch the lives of people across the globe. With great power, comes great responsibility. Thus, it is crucial, Artificial Intelligence that we create as developers are crafted thoughtfully and responsibly. Microsoft has pointed our six different principles to abide by as the guidelines while designing our Artificial Intelligent Systems. 

The six main principles of Responsible AI are as listed below. 

Fairness 

AI system should be fair to every entity. It cannot prefer one entity over the other based on ethnicity, gender, nationality, or other superficial factors. This fairness if not taken into care, can create havoc in judicial decision-making scenarios such as law, justice, and criminal case analysis among others. Unfair advantages and disadvantages must both be taken care of while developing an AI system. 

Reliability and Safety 

In critical use case scenarios such as healthcare, autonomous driving – the reliability and safety of AI is paramount. The AI system must go through rigorous testing and approval before it can be in use. 

Privacy and Security 

AI system must be secure and it must respect the privacy of individuals. Data leaks cannot occur in the AI system. Since AI models heavily rely on data to better themselves, it is important that security and privacy concerns are taken care of. Some of the points to note during designing AI with privacy and security in mind are, 

• Data Origin and Lineage 

• Data Use – Internal vs External 

• Data Corruption Considerations 

Inclusiveness 

AI should be inclusive. It must engage and empower people from wide aspects of society. After all, AI is here to help and give better lives to humans. Multitudes of demographics must be considered while developing an AI system such that no single group is left out or is made to feel disempowered. 

Transparency 

The AI systems that we developed should be transparent. The users of the system should be enabled and made aware of the purpose of the system, its working protocols, and processes, and its expected limitations. This will give a transparent outlook to the user side so that their data are not manipulated and the decision-makings supported by the AI system in different use cases are indeed transparent. 

Accountability 

AI systems should be designed with Accountability in mind. The developers and architects of the AI system need to work within the framework of governance and organizational principles such that the system can be relied upon by every user. The solutions that are provided must be ensured to be ethical and up-handle the legal code of standards. 

Model Questions for AI – 900 Exam

1. You need to build an app that will read your newspaper aloud to support users who have reduced vision. Which service in Azure should you use? 

A. Text Analytics 

B. Translator Text 

C. Speech 

D. Language Understanding (LUIS) 

2. Your website provides a feature of a chatbot which is dedicated to serve customers. You are required to detect the emotional situation of a customer for instance, happy, sad, neutral based on the input typed by the customer in the chatbot. Which type of AI workload should you use? 

A. Anomaly Detection 

B. Semantic Segmentation 

C. Regression 

D. Natural Language Processing 

3. Complete the Workload Type as per the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. 

4. Complete the Principle as per the column on the left to the description on the right. Each principle type may be used once, more than once, or not at all.

5. Choose the three out of six guiding principles for responsible AI that Microsoft abides by? Each correct answer presents a complete solution. 

A. Knowledgeability 

B. Decisiveness 

C. Inclusiveness 

D. Fairness 

E. Opinionatedness 

F. Reliability and Safety 

6. What example of computer vision would be creating a bounding box around the vehicle identified in an image? 

A. Image Classification 

B. Object Detection 

C. Semantic Segmentation 

D. Optical Character Recognition (OCR) 

7. You are creating an app that can count the total number of people in a stadium. You need to ensure their face is visible. What should you use to analyze the images? 

A. the Verify operation in the Face service 

B. the Detect operation in the Face service 

C. the Describe Image operation in the Computer Vision service 

D. the Analyze Image operation in the Computer Vision service 

8. A training dataset and validation dataset from an existing dataset needs to be created. 
Which module in the Azure Machine Learning designer would you suggest should be used? 

A. Select Columns in Dataset 

B. Add Rows 

C. Split Data 

D. Join Data 

9. Complete the Learning Type as per the column on the left to the description on the right. Each Learning Type may be used once, more than once, or not at all. 

10. Complete the Task as per the column on the left to the description on the right. Each Learning Type may be used once, more than once, or not at all 

11. Which type of Machine Learning should be used to predict the total revenue generation for the next year looking at past data of 10 years? 

A. Classification 

B. Regression 

C. Clustering 

D. Anomaly Detection

12. Choose the Yes/ No Radio Button for each of the statements.

Answers 

1. C 

Explanation: The Text to Speech feature of Cognitive Services of Microsoft Azure helps to bring apps to life with natural sounding voices. The reading of newspaper from digital format text would be transformation of text to speech and thus, the use of Speech which is Answer C.

2. D 

Explanation: The understanding of Emotional Situation can be processed by the Sentiment Analysis in amalgamation of language detection, topic detection and key phrase extraction. All of these are tasks performed by Natural Language Processing.

3. These answers can be easily be decided once you’ve read this article from top to bottom. Check into the various segments of Computer Vision, Natural Language Processing, Conversational AI with its sub topics discussed above.

4. These answers can be easily be decided once you’ve read this article from top to bottom. Check into the various principle under the Responsible AI to learn more that are discussed above.

5. C, D and F. This would be a multiple choice question with check boxes to choose.

Explanation: We know, the six principles of Responsible AI are Fairness, Reliability and Safety, Privacy and Security, Inclusiveness, Transparency and Accountability. Each of these principles are discussed in detail above in the article. 

6. B 

Explanation: Among different sub topics on Computer Vision above, we discussed about object detection. Object Detection is performed by training models to classify individual objects within the frame of the image or video. With object detection, we can detect various images within an image, for what the model is trained for. For creating a bounding box around a vehicle, the vehicle should be detected in the image which is an example of object detection. The image below would help visualize what the question asked.

7. B 

Explanation: The Face Service of Azure can provide multitudes of usage. However, it needs to be understood the difference between Face Detection and Face Recognition. The Question clearly asked only about counting the people whose faces are visible which is the problem of face detection. Face detection as the name suggests in a technology using which human faces can be detected in images, videos, and other digital forms. This has huge implications for systems from airports to shopping malls. Its applications and usage are only limited to human imagination. Face Recognition on the other hand is the capability of a system to recognize and distinguish a specific person through matching the digital image or video from the database of faces. From usage for security to unlock phones to identify people in social media platforms like Facebook, face recognition has huge implications. Thus, the Detect operation of Face Service is used.

8. C 

Explanation: In Machine Learning, an existing dataset should be pre-split for training and later for validating and testing to configure the accuracy. This is essential so that the accuracy of the system is not tampered with. If training and validating is done in same data without splitting earlier, the accuracy would come out higher than what really is, which is unethical. Thus, The Split Data feature in the Azure Machine Learning Studio should be used.

9. First and foremost, we need to understand what Regression, Classification and Clustering is. Regression is a statistical approach to estimate relationship between a dependent variable and one a more independent variable. It is a supervised learning technique and can be used for the prediction of continuous value such as house price prediction. Classification Model is also a supervised machine learning technique which focuses on predicting classes and categories from inputted data. From Spam Detection, Dog Breed Detection to Sentiment Analysis, Classification Model plays a huge role. Clustering falls under unsupervised machine learning which focuses on grouping entities that are similar to each other based on feature. Algorithms such a K-Means can be used to cluster images based on colors.

When we can have learnt what each of these learning types mean, the approach to solving the problem can be identified with ease. The numerical value prediction for minutes that is caused by Snowfall is a linear relationship with dependent and independent variables, which is Regression.

The description itself describes segmenting customers into different groups. The grouping itself clearly explains that, Clustering is the approach to choose here. The answer is always hidden in the question itself. With proper reading and analyzing, the right option can be chosen with practice.

The prediction of Simple – Yes / No Question is a Classification Problem. Similar to if a dog falls on one breed or other, prediction if a student will complete a university course would be a Classification Problem.

10. This Drag and Drop Question will feel easier once you have the technical understanding of what each Learning Types mean. The answer to this question is following.

Confusion Matrix 

In most supervised learnings, the visualization of the performance of the algorithms is done using a confusion matrix which displays the error matrix based on statistical classification. It gives value to the level of confusion in the data analysis. In Azure, the Model Evaluation module outputs the matrix with various numbers for ROC, Precision/Recall, Lift Curves and Mainly the True Positive, True Negative, False Positive and False Negative Value. 

Feature Engineering 

Feature Engineering refers to the process of using data and its specific domain knowledge to support Machine Learning algorithms learn in a better way. Splitting the date into months, days and year helps create features from this raw data. Its looking confusing how the timestamp alone can be no use at times for Machine Learning, but once divided into different parts, each individual unit of the splitted data can be used for creation of new features. Holiday Dates, Working Dates can be figured out once the data is split.

Feature Selection 

Feature Selection is the process of selecting features for building analytical models from the subsets of relevant data. Temperature and Pressure in this question refers to similar feature selection in order to train the weather model.

11. B. Regression.

Explanation: This is a question of predicting a numerical value. With the past data of 10 years, a regression model can be designed to predict what value can be extrapolated upon for the next year. Thus, Regression is the answer of this question. Whenever, relationship can be established between dependent variable ie. A numerical outcome and other independent variables, Regression will help us achieve our prediction goal.

12. The answers are as shown below with the Green Boxes. 

It has been clearly discussed above in this article, the price of house prediction is a Regression Question as the independent and dependent variables relationship can be developed through it.

Anomaly detection instead helps in finding out anomaly ie. Usually detection of errors and unusual activities. Anomaly basically means something beyond normal such as in detection of errors and failure, fraudulent system detection and so on. Thus, suspicious looking sign-ins that are deviated from trended patterns can be found by anomaly detection.

 
Furthermore, the prediction of patient developing diabetes is a Yes/No question which falls under Classification problem rather than Anomaly Detection. Algorithms such as Naïve Bayes would provide a higher accuracy result for this kind of problem.

Conclusion

Thus, in this article we went through a thorough learning for Microsoft Certified: Azure AI Fundamentals Certification through the AI 900 Exam. We covered on what this exam really is, how this certification can open doors for prospective candidates, a rough pre-info about the exam from scheduling to about passing scores and the details about the Proctor in brief before taking the exam. From there onwards, we discussed about the process to learn and prepare for the AI-900 Exam and the relevant resources on Microsoft Learn. Then, we discussed in brief about the various topics that are covered in Exam AI – 900 and supported you with an overview of various knowledge necessary for the exam from Basics of AI on Azure, Azure Machine Learning, various types of Machine Learning and features supported by Azure such as Computer Vision, Natural Language Processing and Conversational AI. Thereafter, we also discussed about Responsible AI and its principle. At last, 12 Model Questions for AI – 900 Exams were discussed. These questions are very similar to what the exam would be like and what one can expect. Model Questions were listed above with the Answers and Explanations were described in details for why we opted for the best suitable answers from the multiple choices. Hope you loved this article and wish you readers the very best for your upcoming exam.