How to pass the AWS Machine Learning Specialty exam

When I prepared for my AWS Machine Learning Specialty certification, I did not just want to pass, but I wanted to walk into the exam room knowing exactly what to expect.

I wanted to sit for the AWS Machine Learning exam with the same confidence I had when I passed the Scrum Master and Product Owner exams with near perfect scores.

Over time, I developed a repeatable strategy that I have now used to help me pass multiple IT certifications, including the AWS ML Specialty exam. If you are interested in passing the exam yourself, here is a five step strategy that will help you do it:

Read and focus your study on the official exam objectives
Take practice exams before you start formal study
Commit to a structured course from a reputable trainer
Do hands on projects that reinforce generative AI concepts in AWS
Dedicate the final weekend to multiple rounds of practice exams

Add on a sensible exam day strategy, and you will greatly enhance your chances of passing the AWS Machine Learning Specialty exam on the first try.

Here is how I applied this strategy specifically for the ML Specialty exam.

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Step 1: Read the exam objectives

Before starting any study, go directly to the official exam guide. It explains the domains, the weighting of each, and which services are in scope for the MLS C01 exam.

The objectives highlight the four key domains which are data engineering, exploratory data analysis, modeling, and ML implementation and operations.

Reading the objectives first gave me clarity and helped me avoid wasting time on areas not covered by the exam.

Step 2: Do practice exams before studying

Start with practice exams even before opening a textbook or course. This shows you how AWS frames questions and immediately reveals your blind spots.

You will see which services and concepts appear most often. For the machine learning certification exam these often include SageMaker, Glue, Kinesis, and concepts like feature engineering and model evaluation.

By exposing yourself early, you prime your brain so that later study feels more familiar.

Step 3: Take a course

Once you know your weak areas, take a structured course. AWS Skill Builder and AWS Academy offer free training, while platforms like Udemy provide deeper dives into ML Specialty content.

I paired a free YouTube playlist on ML basics with a paid step by step Udemy course that focused specifically on the MLS C01 exam. This combination gave me both official content and practical insights.

Step 4: Do hands on projects in the AWS console

Reading is not enough. You need to build small ML projects in the AWS console. These projects do not need to be complex or expensive but they should reinforce the core exam topics. For example:

Build an end to end ML pipeline in Amazon SageMaker from data ingestion to model deployment.
Use AWS Glue to clean and transform raw datasets before training.
Stream real time data with Amazon Kinesis and apply transformations for ML analysis.
Experiment with feature engineering and visualization using Jupyter notebooks in SageMaker Studio.
Deploy a trained model as an endpoint and test it with sample inputs.

These projects build intuition and prepare you for scenario questions that ask which AWS service or design is best for a given ML workload.

Step 5: Get serious about mock exams

Once your study is complete, dedicate time to repeated mock exams. I spent full days cycling through practice exams, reviewing results, and retesting until I consistently scored well above the passing mark.

Mock exams are not just for memorization. They train you to recognize AWS certification exam language and identify distractor options quickly.

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Your exam day strategy

On exam day, mental preparation is as important as knowledge. These approaches kept me focused and confident under pressure.

Read each question carefully and watch for keywords such as most efficient, lowest cost, or highest availability.
Eliminate obvious wrong answers quickly. Two of the four options are often clearly incorrect which narrows your choices.
When possible choose managed AWS services like SageMaker or Glue since the exam favors solutions that reduce setup and maintenance.
Do one pass through all questions answering what you know, then return to flagged questions in a second review.
Always answer every question. Even a guess gives you a chance to score.
Track your time and aim to finish the first pass with at least twenty minutes left for review.
Look for clues in later questions. Sometimes the wording helps clarify an earlier problem.

By pacing myself this way I was able to make two complete passes through the AWS certification exam, catch small mistakes, and walk out feeling confident. These steps minimize risk and improve your odds of passing the ML Specialty exam on the first attempt.

Darcy DeClute is an AWS Certified Cloud Practitioner and author of the Scrum Master Certification Guide. Popular both on Udemy and social media, Darcy’s @Scrumtuous account has well over 250K followers on Twitter/X.

So what’s next?

A great way to secure your employment or even open the door to new opportunities is to get certified. If you’re interested in AWS products, here are a few great resources to help you get Cloud Practitioner, Solution Architect, Machine Learning and DevOps certified from AWS:

Put your career on overdrive and get AWS certified today!