Mastering Azure AI Certifications: Passing the AI-900 and AI-102 Exams 1st Time
If you’re aiming to conquer the AI-900 and AI-102 exams, the Microsoft Azure certifications that validate your expertise in designing and implementing AI solutions, you’re on the right track. These certifications are highly sought after in the tech industry and passing them can open doors to exciting career opportunities. In this blog, we’ll go through the process of passing both exams, starting with the AI-900 and then moving on to the AI-102. We’ll then finish with some comparisons between the two and some overall takeaways. Let’s dive in!
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AI-900: Designing and Implementing an Azure AI Solution
The AI-900 exam focuses on designing and implementing AI solutions using Azure services. Here are some key points to help you prepare and pass this exam:
Understand Azure AI Fundamentals: Begin by familiarising yourself with the core concepts of Azure AI, including cognitive services, machine learning, and natural language processing. Microsoft Learn provides excellent resources to get you started. The AI-900 study material provides a really useful overview of how Microsoft got to the point it’s at now in the field of AI, and what we can expect in the future. Reading through this material will help pique your interest and set the scene for the more specific areas that follow it.
Dive into Cognitive Services: Cognitive Services are a crucial component of AI solutions. Explore various services like Computer Vision, Speech, Language Understanding, and more. Understand their capabilities, use cases, and how to integrate them into applications. It’s worth noting that this is a very important area on both the AI-900 and AI-102 exams. In a nutshell, you will be presented with specific business scenarios and will be asked to identify which type of Services should be resourced within Azure to meet the requirements. Certain scenarios can be achieved by more than one service, which means other practicalities such as cost, and ease of integration should also be considered.
Grasp Machine Learning Concepts: For the AI-900 in particular, it is important to understand model types such as regression analysis and classification. Again, you will be presented with scenarios in which you will be asked to put forward the most appropriate solution, so it is important to have a solid understanding of these machine learning models, algorithms, and techniques. Surprisingly, this is not as much of an important area in the AI-102 exam.
AI-102: Designing and Implementing a Microsoft Azure AI Solution
The AI-102 exam focuses heavily on designing and implementing AI solutions that meet business requirements. It comes with an assumption that you are reasonably grounded in the underlying theory and the questions are much more straight to the point. Having said that, it’s very important to note that although the AI-102 exam is discussed as being more practical and hands-on than the AI-900 exam, the exam itself still consists of multiple-choice questions only.
The Microsoft Study Guide does offer you the chance to practise scenarios in a lab environment, but this is not compulsory for the exam. Furthermore, the exam itself is an open book! There is a ‘search feature’ in the corner of the exam pane which allows you to search across all the Microsoft Study Material. However, Microsoft is quick to censor anything that will provide you with an immediate answer to the question you are being asked, so be aware of this.
Here’s how you can excel in this exam without just repeating what you’ve already studied in AI-900:
Embrace Advanced AI Concepts: AI-102 builds upon the foundation laid by AI-900. Dive deeper into advanced AI concepts like reinforcement learning, deep learning, and anomaly detection. Understand how to leverage these techniques to solve complex business problems. These concepts are the same ones that appear on the AI-900 exam, but the main difference is that you will be asked to apply them in more niche and technical scenarios.
Explore Azure Machine Learning Pipelines: Azure Machine Learning Pipelines enable you to create end-to-end workflows for building, deploying, and managing machine learning models. Familiarise yourself with this powerful tool and learn how to orchestrate ML pipelines effectively.
Master Conversational AI: Conversational AI is a rapidly growing field. Learn how to design and implement chatbots using Azure Bot Service and Language Understanding (LUIS). Understand how to integrate chatbots into various channels and enhance their capabilities. In my own experience and of those who I have spoken to who took this exam, this appears to be a more tested area than others. Make sure you have a solid understanding of everything covered in this area, and that you have done a lot of practice questions on this topic.
Dive into Responsible AI: Responsible AI is gaining prominence, and organisations are increasingly concerned about ethical considerations. Study the principles of responsible AI, including fairness, transparency, and accountability. Learn how to address bias and ensure ethical AI practices. This is an area that comes up less than the others but is important nonetheless! There will be at least one or two guaranteed questions on this with very unambiguous answers. The questions shouldn’t vary much from those found in practice, so make sure you have prepared for them.
Understand Deployment and Monitoring: Gain expertise in deploying AI solutions at scale and monitoring their performance. Learn how to use Azure DevOps, Azure Kubernetes Service (AKS), and Azure Monitor to manage and monitor AI solutions effectively. This is an area that can be tricky for those who don’t have a background in Data Engineering as there is quite a lot of technical lingo which isn’t very intuitive if you’re not familiar with it. The questions that typically come up in this area are tricky because they often ask you to state in which order you would carry out certain steps to implement a solution. It is worth singling out practice questions which deal with these areas as it’s not something that you would automatically know just from looking at the study material.
Key Takeaways
While AI-900 focuses on model types such as regression analysis and classification, AI-102 surprisingly doesn’t emphasise this as much. However, both exams heavily focus on the types of services required in different scenarios.
The AI-900 exam contains a lot more questions to which you could logically arrive at the correct answer by process of elimination of the incorrect answers. This is not the case on the AI-102 exam, where you cannot rely on intuition as much.
As mentioned above, it’s very important to note that although the AI-102 exam is often discussed as requiring completion of tasks in a lab environment, the exam ultimately just consists of multiple-choice questions, similar to the AI-900 exam. While studying for AI-102 provides an opportunity to apply some of the services practically, it is not necessary for the exam itself.
Overall, I would say that for the AI-900 exam, there is a much more direct connection between the study material and the exam questions. For AI-102, although the study material is interesting and useful, my main criticism is that it just doesn’t naturally follow that you would pass the exam from learning it all. For example, the study material shows some examples of API-Syntax in the context of a use-case scenario, however, it is quite a stretch to then be able to (in an exam setting) select the correct piece of API Syntax for a completely different function in a completely different scenario, which brings us to the most important point:
Practice Exam Questions are much more important and helpful than we would like to admit! In all honesty, the main reason as to why I passed (particularly the AI-102 exam) was going through as many practice questions as I could. I got these from www.examtopics.com , but there are several other similar sites. I was surprised at how many questions came up on the day which were nearly identical to the ones I had seen on here. It’s important to be aware, however, that going through these questions is made so much easier when in the context of a solid understanding of the topics. It would not be a good idea to just memorise questions, mainly because it defeats the object of developing your understanding, but also from a practical point of view, you will inevitably find yourself unable to pivot when the questions look even slightly different on the real exam.
Conclusion
Passing both the AI-900 and AI-102 exams requires a solid understanding of Azure AI services, machine learning concepts, and design principles. By following the tips outlined above, you can prepare effectively for each exam without duplicating points. Remember to do plenty of practice questions, practice hands-on, explore advanced AI concepts, and stay updated with the latest Azure AI offerings. Good luck on your journey to becoming a certified Azure AI expert!
About the Author
Liam Williamson is a Data Visualisation Consultant here at Version 1.
