Monday, September 25, 2023

AWS Machine Learning – Specialty Certification: Quick Guide, 2023

After renewing the AWS ML Certification with a perfect score, I got many messages asking about how I prepare the certification(s) and the resources(s) I use. You can find it all over my blog; I use boundless preparation.

What does it mean? Preparing yourself without limitations, using all the resources you need to complete your journey/course/preparation. Limiting yourself to one resource, a prepacked course or book limits your experience to that particular course and path. Remember that many classes or books are built around a specific set of questions that may appear or not in the exam, and the groups of questions constantly evolve. The set I got was almost completely different from the last time. Certainly, I got questions outside the official and unofficial preparation, which are well-known in the specific domain. So, it’s fair game for professional certifications.

I understand not everybody has the same goals or inclinations or the time to invest. So use whatever works for you, but I don’t recommend using just a course and set of practice questions to achieve any technical certification. That’s just an intellectual exercise that will give you limited and temporary results unless you are already an expert in that domain. That also leads to disappointment for many who don’t get the magical results the hype promises. So, deep dive into the aspects that excite you the most or the ones you lack experience with, and expand all you need.

It would be best if you always maximize your investment in these certifications; they are expensive in terms of time and money, so tailor them to fit your needs, not only to get “the badge”.

Guided by the Light(house)

This certification – and domain – comes with its prerequisites and challenges. I understand that ML is a hot topic right now, so many people will try it for the first time. If that’s your case, you have a challenging journey ahead. This is one of the most complex certifications and domains out there, and that’s because to be successful – and have some real working knowledge, not just a badge – you need a proper understanding of the following, but not limited to:
– Python, Pandas, Data Analytics, Data Science, AWS, AWS ML Services and MLOPS

Disclaimer, I’m not new to ML or Analytics; my real journey in the cloud started in 2015, thanks to the release of TensorFlow as an open-source library by Google. I assisted at a Google conference in Madrid that year about deploying Tensorflow in the cloud, which blew my mind. I was using stuff like Spark and MongoDB on-premises, and for the first time, I saw RNNs translating, computer drawings and autonomous driving cars. At that time, the LLMs didn’t exist; I think it took another three years for the first model to be released. I had been using the cloud since 2011 to deploy web services and apps, but that was the real thing!

So, my first recommendation is to invest time in the subjects you don’t have experience in. For instance, I strongly recommend achieving first the AWS Analytics certification or having the equivalent experience. It will help you in a big way, not just for the test. Same thing with Data Science; if you don’t have any experience, you should invest time and resources in learning the basics before anything else.

Remember, this is a speciality certification, which means that, at the very least, you should hold one accreditation at the associate level or the equivalent knowledge. There will be AWS-focused questions about Security and Networking, mostly related to Sagemaker or AI Services, but still.

So yes, lots of stuff, enough to make your head spin if you are new to this subject matter. Well, we can use some resources

to guide us in the vast sea of knowledge of this technological age, and it’s just starting 🙂

Back to Basics

My suggestion is to go and check the AWS available resources first, there are plenty, and they are quite good. As a main resource, I recommend the AWS Official Guide to help you pass the exam, not learn ML. For that, you will need to take other courses and certifications and get some practical experience, as I mentioned before.

The guide seems slim and basic if you have a quick look. But it’s packed with useful information and contains a summarized version of a good portion of the information you’d need to pass the test. It will take you a while to go through it back to back – and you should.

The guide also contains:

  • Assessment test that is useful to gauge your present level
  • Practice questions that are also available online – relevant when writing this post, July 2023. Expect more lengthy and complex questions in the exam, though
  • Includes many additional resources that you should check

Unfortunately, it doesn’t contain any practical exercises, so you need to look elsewhere for that. As I mentioned, the guide includes much information, but in a summarized way. Hence, it’s a good option for people that don’t want or need to go very deep, but I’d recommend using other resources to complement it; it could be a video course, documentation, Internet resources or a book like “Hands-On: Machine Learning with Scikit-Learn, Keras & TensorFlow.”

I own two physical editions of the book, the first and second editions, and I can tell you that it is the perfect blend of textbook explanations with hands-on examples. Excellent to learn about the algorithms in deep coupled with Python-based examples. It’s cloud agnostic until the last part of the book, which focuses on GCP.

Let’s go back to the AWS resources:

AWS Certification Site: the primary site contains some good resources for preparation, including sample questions, the official practice test and the interactive readiness course, which also includes lots of additional questions.

Let’s have a look at some relevant questions for the test, taken from the sample questions, AWS’s property 🙂

The above question focuses on analytics; as I mentioned, you need a good level to pass the test.

The question focuses purely on data science – it will be the same on any platform.

Let’s see another flavour taken from the AWS readiness course:

This interesting question focuses on AWS ML Engineering, improving the training performance on Sagemaker.

As you can see, you will confront very different types of questions, which makes the test much harder. The AWS official resources contain many questions; if you still need more, you can find additional sets on Udemy. I took some of them updated in 2023, which are relevant, but I wouldn’t say they are essential. It depends on your case; if this is your first or second AWS certification, or you are new to the subject matter, then yes. For me, it was redundant.

More resources:

AWS ML blog: excellent site to read about actual use cases, technical articles and new features.

Some useful posts:

AWS Training and Certification Blog

A post about a new AWS course focused on the ML lifecycle:

AWS Documentation: an obvious one, but still the best source of information


This is a complex certification that you should prepare accordingly. Take your time and prepare for your journey, not just the destination.

Good Luck!

Adolfo Estevez
A Estevez
Cloud & Digital Evangelist | AWS x 13 Certified | GCP x 6 | Serverless | Machine Learning | Analytics |

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