Acing the AWS Certified Machine Learning Specialty Exam like a Pro

Last Updated on September 20, 2020 by Editorial Team

Author(s): anuragbisht

Cloud Computing, Machine Learning

Certification Preparation Guide 2020

I hope you all are doing well. In this article, I would like to share my experience of passing the AWS machine learning specialty certification exam. The objective of this post is to help folks who would like to pursue their career in the field of data science or machine learning and want to showcase their interests. At the end of this post, I will highlight a couple of points regarding this certification exam, which you can use as a hint about whether or not you should go for the certification.

Level of difficulty:

I found some of the exam questions to be tough and confusing at times, especially the ones with multiple answers and single select answers with at least two identical options. Overall the questions thoroughly check your ability to decode the problem statement into machine learning solutions with the best possible approach, knowledge about the ML life cycle phases, real work exposure, best fitted AWS service, and deep learning knowledge in some questions and last but not least your foundational knowledge about machine learning and data science.

If you already know machine learning but are not aware of cloud computing, it is recommended that you first build a base knowledge about the AWS platform using the AWS Solution Architect Associate certification, although not mandatory.

If you have already passed the associate exam, then don’t expect questions of this exam to be straightforward. Treat this exam to be tougher than the associate exam and prepare well. Rarely you will find questions where you can figure out the answer only based on guesswork or elimination.
So, how can you prepare for this exam? Here is a quick guide from my experience.

Prerequisites before taking the exam:

The exam involves :

1. Cloud computing knowledge, especially general operational excellence principles.
2. Knowledge of AWS services as this certification involves designing scalable machine learning solutions using the AWS platform.
3. Data science and machine learning concepts and real-world knowledge.
4. AWS Sage-maker and machine learning services.
5. Ability to translate a problem into a machine learning solution.
6. Knowledge about the end to end machine learning life cycle phases and algorithms.
7. Examination structure and planned preparation to tackle the exam.
8. This should have been at the top, money :P, the exam costs USD 300 plus some taxes.
9. A functional PC/laptop with webcam and microphone for proctoring the online exam.

Let’s discuss each of the points above on how you can plan and prepare with relevant sources.

1. Cloud computing knowledge is really important nowadays considering the number of services offered and the growing popularity of migration from on-premise to cloud services. There is n number of benefits but for now, let’s not lose our focus. To check this point, you can either take go through the AWS well-architected framework whitepaper link. You can even go one step further and learn in detail about each pillar through specific guides and documentation.
2. This point involves all the AWS services that are involved in building large distributed data platforms, including machine learning and data engineering services offered by AWS. I would highly recommend the following links. You can go through each service and understand their role and when to use them. Link1 Link2 Link3
3. This is one of the crucial and time-consuming points which will require some patience in understanding the machine learning concepts. You can go through machine learning documentation from amazon through this link. Make sure you keep patience and understand each concept properly; otherwise, you won’t be able to tackle almost most of the exam questions properly.
4. Amazon Sage-maker can be used to easily manage most of the life-cycle phases of machine learning on the AWS platform, excluding the data engineering part. You must understand how it works, and what all algorithms can be used, what hyper-parameter can be used. You can thoroughly go through all the documentation from this link.
5. The ability to translate a business problem to a machine learning solution is a very important skill that is acquired with experience. But if you are bad at it, I would suggest you research the machine learning use-cases and start decoding the use-cases to ML solution. Also, you can go through the AWS Machine Learning Specialty exam readiness course with a practice exam to get an idea about the questions. Take the practice exam questions as a baseline but expect difficult scenarios in the main exam.
6. The exam also assesses your knowledge about the end to end machine learning life-cycle. You can learn about that in one of the offered courses in the following learning path link.
7. The exam structure is divided into four domains: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Each section has a particular weightage in the exam, so make sure you plan accordingly.

Also, I would suggest you taking an AWS official machine learning specialty practice exam to get a good idea about the actual exam questions.

8. Now can register for the exam using this link. You have to create an account. Then you have to navigate to the account->schedule exam->AWS Certified Machine Learning — Specialty

You have two options to schedule an exam :

Pearson VUE exams, PSI exams. I selected Pearson as I have already given exams through Pearson. You will have to select the exam option (online proctored or test center).

Note: You can schedule exams in advance, but there is a rescheduling limit of 3 times only with some terms and conditions about charges in case within five days.

9. Now you have a scheduled exam, make sure you have a laptop/ PC with a good quality webcam, uninterrupted internet, and microphone. You have to download the software and make sure it works properly. I would highly suggest you set up the software and check the above requirements at-least 30 minutes before the exam.

In the end, I would like to highlight a couple of points:

1. This exam will test a lot of concepts and your ability to build a machine pipeline. Hence prepare well. All the above points are important, so don’t skip any of them.
2. Give practice exams, especially the official practice exam, so that you don’t expect the unexpected.
3. Take this exam only when you are fully prepared.
4. This exam will validate your passion, interest, and understanding in this field. It will certainly guarantee to showcase your interest highlighting your profile but still, you have to deliver results in real-world projects/ interviews.

Best of luck with your exam!

If you liked this post, then please do consider following me for good quality content and tutorials on AI/machine learning, data analytics, and BI.

Check me out on LinkedIn

Acing the AWS Certified Machine Learning Specialty Exam like a Pro was originally published in Towards AI — Multidisciplinary Science Journal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Published via Towards AI