How you can score 100% on the AWS Machine Learning Specialty Certification - 2025

A guide to mastering AWS ML Specialty Exam

Sep 29, 2024

Introduction

A few weeks ago, I took the AWS Machine Learning Specialty Certification exam and, to my surprise, I scored 100% — I wasn’t even sure scoring 100% was possible! After sharing this achievement on LinkedIn, I received many messages from people asking for advice and guidance. Though I responded to everyone, I realized that it might be more helpful to share my full experience with a broader audience.

So here it is — my journey and the steps I took to achieve this!

And yes, if you didn’t already know—scoring 100% on an AWS certification exam is indeed possible! Below is the proof 😉

If I can DO IT, so can YOU!

When I first thought about attempting this certification, everyone — yes, even the internet was saying things like:

“It’s the most challenging, difficult, not so rewarding,” and all sorts of other negative CRAP.

But I’m here to tell you something different:

“It’s NOT that difficult, YOU can absolutely DO it, and YES, it IS rewarding!”

It rewards not just with respect to career progression but also in terms of your self-confidence. So pleaheasseeee continue reading, and hear me out!

I get it — preparing for the AWS Machine Learning Specialty certification is exciting but can also feel overwhelming at times. The good news is, with the right approach and mindset, it’s absoooolutely achievable. Whether you’re just starting or already familiar with AWS, remember: it’s all about taking small, consistent steps.

Keep in mind that this certification isn’t just another checkbox. It’s about testing your knowledge to build scalable machine learning solutions and applying those skills in the real world. Think of it as a long-term investment in your career, with the certification being the cherry on top! 🍒

What Is This Exam About?

The AWS Machine Learning Specialty certification is designed to assess your ability to build, train, and deploy machine learning models using AWS services. It tests your skills across below domains:

Data Engineering: Understanding how to create and maintain data pipelines.
Exploratory Data Analysis: Techniques for analyzing data and preparing it for modeling.
Modeling: Choosing, training, and tuning models for different use cases.
MLOps: Deploying, monitoring, and automating machine learning models at scale.

Most importantly, this exam is about practical, real-world application. It doesn’t just test theory but challenges you with scenario-based questions that mimic what you’d encounter in a real ML project. You’ll come across questions related to services like Amazon SageMaker, AWS Glue, Lambda, S3, and more.

For example, you might be asked whether to use SageMaker Data Wrangler or AWS Glue for a data transformation task, or whether to deploy your model using a real-time or asynchronous SageMaker endpoint. You’ll need to know not only the right answer but why it’s the best approach in that particular scenario.

Similarly, you might be asked how to handle class imbalance in a fraud detection model where most transactions are legitimate. Should you use SMOTE to oversample the minority class or adjust the model’s parameters to place more weight on false negatives?

The key is understanding real-world trade-offs and applying the right solution for practical problems.

My Preparation Journey

When I began preparing for this certification, my goal wasn’t just to pass — I wanted to test my knowledge and identify any areas where I could improve. Having worked in the Data Science field for six years, with two years of experience using AWS and a year working on Google Cloud Platform before that, I felt ready to take on this challenge.

I aimed for a consistent, manageable study schedule of 1-2 hours daily for about a month, or until I was consistently scoring 80% on mock tests. This approach helped me cover all the necessary topics without feeling overwhelmed.

Courses I used

I didn’t want to fall into the black hole of endless amounts of information — referring to 10 different courses or 20 different blogs can easily become overwhelming. I wanted to keep my preparation as lean and consistent as possible. So, I decided to stick with just one resource!

I found AWS Machine Learning Specialty course by Frank Kane and Stephane Maarek on Udemy to be regularly updated and very closely aligned with the certification syllabus. So I just chose and started with it!

My goal wasn’t to rush through the course or finish 100% of it. Instead, I aimed to enjoy the process and truly understand the material. Since many of the topics were familiar to me from my experience, I could easily connect what I was learning to real-world machine learning tasks I had already worked on.

I also used this opportunity to dive deeper into specific topics that interested me. For example, I watched additional content on building ML systems using AWS services for various use cases, and strengthened my understanding of concepts like hyperparameter tuning using AWS and SageMaker Pipelines.

Practice Tests - how much practice is enough practice?

Before diving into endless practice exams, first ask yourself: Have I truly understood the services and concepts? Can you apply what you’ve learned in real-life scenarios? If the answer is yes, then you’re on the right track!

Here are the practice resources I used during my preparation:

Out of these, ExamTopics was by far the most helpful. It offers a wide variety of high-quality, scenario-based questions that closely resemble what you’ll encounter on the actual exam.

Exam Day Tips

I took the proctored exam from home, mainly because I was feeling spontaneous about giving it when I felt well-prepared. Unfortunately, there were no dates available at a test center at the time. In hindsight, if I had planned better, I would have preferred taking the exam at a testing center for a stress-free environment.

Tip: If you plan to take the test at a test center, make sure to book your seat at least 24 hours before your desired exam time. To get your preferred time slot, booking a couple of weeks in advance is recommended.

Don’t be surprised - the exam questions are pretty long!

Here are a few recommendations I stuck to during the exam time.

Read Carefully: Take the time to fully read each question. Some questions are long because they include a detailed scenario, and the answer might depend on a small detail hidden in the text.
Focus on Key Phrases: Look for key phrases like “most cost-effective,” “fastest throughput,” or “low-latency.” These phrases often point you toward the correct answer by clarifying what the main objective is.
Trust Your Instincts: Often, the correct answer will come to you right after reading the question. Trust your instincts, especially if you’ve prepared well. Overthinking can sometimes lead to doubt.
Flagging Questions: If you’re unsure about a question, flag it and move on. You can revisit it later, and sometimes other questions will provide hints or help jog your memory. There is enough time per question — 2 to 3 minutes, so take your time and don’t rush.
Eliminate Wrong Answers: If you’re completely unsure about a question, start by eliminating the answers you know are incorrect. Narrowing down the options makes it easier to focus on the most plausible answer.

Remember, the exam is not trying to trick you — the questions are straightforward. Approach them with a clear and logical mindset.

Tip: If English is not your first language then you can request for an ESL and get extra 30 minutes for the exam.

For Beginners: How to Get Started and Succeed

If you’re feeling overwhelmed about diving straight into the Machine Learning Specialty certification and haven’t spent much time working with AWS services, I recommend starting with one of the following:

AWS Certified Machine Learning Engineer - Associate: AWS recently introduced this certification, and it’s less challenging than the Specialty exam, making it a great stepping stone.
AWS Certified Cloud Practitioner: This beginner-level exam introduces you to all the key AWS services and gives you a solid foundation to build on AWS.

Tip: Not only does this certification make the AWS learning curve easier to manage, but once you pass the exam, you’ll receive 50% off on your next AWS certification exam.

Building Your ML Skills

For the AWS Machine Learning Specialty certification, having at least two years of hands-on machine learning experience is highly recommended. It’s crucial to apply what you’ve learned in real-world scenarios, whether through personal projects or platforms like Kaggle. Some key focus areas:

Classical and Deep Learning Problems: Make sure you’re familiar with both traditional ML algorithms (like linear regression, decision trees) and deep learning architectures as well as frameworks such as PyTorch, TensorFlow.
Handling Overfitting and Underfitting: Learn how to manage overfitting by using techniques like L2 regularization or dropout in deep learning models. Recognizing underfitting and knowing how to adjust your model accordingly is also crucial.
Addressing Class Imbalance: In scenarios where class imbalance is an issue (like fraud detection), understand when to use techniques like SMOTE for oversampling, or adjust model parameters to focus more on false negatives.
Hyperparameter Tuning: Learn how to optimize models by tuning hyperparameters using tools like AWS SageMaker’s built-in tuning features. This is key to getting the best performance out of your models.
Dealing with Outliers and Missing Data: You should be comfortable handling outliers and missing values in your data, as well as using techniques like PCA to address multicollinearity.
Visualization Skills: Visualizing data effectively is crucial for identifying patterns, dependencies, and outliers. Make sure you can read and create useful visualizations that help inform your data decisions.

Hands-on ML Skills

Work on Kaggle Competitions: Kaggle is a great platform to apply the concepts you’re learning to real-world data. Focus on how to preprocess data, tune models, and evaluate performance. Look into the existing competitions and notebooks.
Use AWS for Projects: Make use of AWS free tier resource access, as well as free workshops provided by AWS and start building end-to-end machine learning systems using AWS SageMaker and other services. Practice training models, deploying them, and setting up data pipelines to automate workflows.
Focus on Key AWS Services: Make sure you’re comfortable with key services like SageMaker, Lambda, S3, Glue, and EC2. Hands-on experience with these tools will not only help you in the exam but also in real-world applications.

Final Tip: Enjoy the Process

Whether you’re preparing for your first AWS certification or diving deeper into machine learning, remember to enjoy the learning process. Take your time to understand each topic, apply it to practical use cases, and don’t rush through it.

Remember that this isn’t just about earning a certification — it’s about building long-term skills that will help you grow as a machine learning professional. The skills you develop here are transferable to other cloud platforms as well. And if you’re just starting out, take it one step at a time — the progress will follow.

Stay consistent, keep learning, and trust in your ability to succeed. Good luck! 🤞

Starting with this post, I’ll be diving into topics like Data Science, Machine Learning, MLOps, LLMs, and more—topics I work with daily in the industry, providing valuable, real-world insights. I’d love to share these learnings with you, so don’t miss out—go ahead and subscribe!