How to Prepare Exam for AWS Solutions Architect - Associate

On 24 January 2020, I was excited to collect the trophy I have been dreaming about: the certificate for AWS Solutions Architect - Associate. While the memory is still fresh, I would like to share my story of how to achieve this. My first-hand experience, I hope, will benefit aspiring learners to get certified.

Why I take the exam

The past couple of years have been a disguised blessing for me. I got redundant at work and had personal difficulties, which explains my discontinued activity in this space. At the end of the day, I landed a job at a great organisation where I am able to add data engineering to my career portfolio (check out why I want to do that: https://doctor-fei.blogspot.com/2020/02/why-i-learned-data-engineering-as-from.html). The organisation encourages and supports constant learning among its employees and, as an AWS partner, values AWS certifications probably the most.

In such an environment, not getting certified needs good reasons rather than getting one: all from the healthy peer pressure. Hence my journey begins.

Be clear about what the exam expects from you

AWS services at the first glance are undoubtedly daunting. By 2020 there are 212 services available. No one can learn everything. Therefore a clear goal is the solid stepping stone to success. Make sure what you are expected to learn for this exam. The exam guideline is surely your good friend.

Have a good teacher

While knowing what to learn is good, a good teacher can make your learning experience enjoyable without the pain. I’m lucky that at my work I have free access to a collection of Udemy courses, where I find the prep course by Stephane Maarek outstandingly useful. It precisely covers all you need to know and also teaches you practical knowledge as well as best practices. The course includes two practice tests which I personally find the most close ones to official tests. The importance of practice tests will be discussed shortly.

Learn efficiently

When it comes to your own due diligence, a smart learner absorbs knowledge with efficiency. You should avoid only following the episodes of the online course and hope for the best. Here are my two cents.

Understand the business problems and how cloud services solve them

Each of the services are developed to solve business problems. Pay close attention to what scenarios a service fits in. A better understanding can dramatically speed up your response to exam questions. A typical question in real exam nearly always starts with a problem, “You work for a start-up. They plan to leverage cloud resources to host their company’s website without long term commitment. What is the best EC2 type for their purpose?” Catching the business related keywords “without long term commitment” here enables you to pick up the correction option quickly.

Hands-on practice

Without hands-on practice, even the best memory can slip off your mind faster than you think. I suggest you open your AWS account and follow all the demos in the course. For one reason or another, you would encounter, at least from my own experience, unexpected errors. Don’t get frustrated when this happens. Conversely I urge you to love and embrace them. To solve the issues you have to google and figure out what it means. In doing this, you most likely read AWS documentations and Stackoverflow discussions. Therefore you learn the nitty gritty of the subject. Additionally, the benefit of this troubleshooting experience can go beyond the exam. The errors will probably appear again in your real work and you have known the solution.

Learn repeatedly

Finally, practice tests boost your performance in the exam. You should time all your practice tests. Learn the pace that is the best for you. At the time of this writing, there are 65 questions to answer within 130 minutes. After a few self-tests, I find myself spending about a minute on each question. In the real exam, I have a slow start but I am not panicking because I know statistically I use less than 130 minutes to answer all the questions and everything is OK. Practice tests can also reveal your weaknesses. Treat what you get wrong like you see an error in hands-on. Dive deeper into the AWS documentations or watch the course video again. Repeating the hands-on when there is one is the best.

Conclusion

That’s all I have got to share. My theory is the general rules here isn’t only helpful to your AWS exams but any exams. I hope you like it. Please ask any questions, leave your comments, or share your success in exams below.

Why I Learned Data Engineering as from a Data Scientist

Report from a true personal story

Disclaimer: The opinions in this article are restricted by the scope of my personal expriences. Please do NOT take it as the only advice for planning your future career.

The first job I got after leaving academia was data scientist. I loved the opportunity of crunching numbers as daily activities. But later I realised I must acquire experience in data engineering. Here’s my true story.

The first company I worked for is a small and fast growing consulting firm at that time. I was the only data scientist there. The company was doing well, landing contracts from renowned Australian brands. The projects mostly involve taking data resources form clients data warehouse or data mart (occasionally from source database, which is crazy) building customer views and setting up online, mainly email, marketing campaigns.

My role was supposed to spice the company’s products with artificial intelligence. In a couple of projects I developed customer clustering models. They group customers into natural clusters based on facts, including demographics, focusing primarily on transactional interactions with the brands. For instance purchase frequencies and volumes. The knowledge learned by the algorithm from data informs the clients about the patterns of behaviours in their customers and helps them tailor messages used in the campaign to different cohorts.

Another type of models useful in marketing campaigns I built is churn prediction. Knowing how likely to lose a customer gives the business advantage to offer promotions or discounts to retain the customers at risk.

All sounds interesting then what is the problem?

Statisticians always warn us by saying “Garbage in, garbage out”. The quality of the data asset is vital to data science project. Interestingly but probably not surprisingly, I found, the managing personnel’s attitude to the value of data science models resonates with the maturity of the data they own.

On some occasions the model was built and deployed into production quite smoothly. On many others, it was built but we never heard from the clients about deployment. It also happened that the data was too poor to dream about any valid model.

Similar things happened in my second job. Once the stakeholders were interested in having a model predicting online traffic volume. Sadly it never turns up on the company’s roadmap.

My story might be discouraging for those aspiring to be data scientist but it happens for good reasons. In my opinion, the profound reason is data science modelling as a new comer resides at the end of the data processing pipeline. Normally a pipeline starts from reading data from source systems, transforms it, and stores it in data warehouse to serve reporting views or data marts and maybe the machine learning models.

There are easy to imagine consequences out of this topology. Developing a working model draws least attention during planning meetings, despite people might talk about it a lot when brainstorming for a project. For many projects in this country, as I observe, a machine learning application is something good to have but not essential. Presumably, this is largely influenced by today’s decision-makers who received their education when informative dashboard reporting business performance was the universe. Take-off of the algorithm based models in business will need patience and time.

While I was struggling to prove my value as a data scientist, another role has been too busy to argue about their importance. They are the sometimes behind-the-scene heros who build the pipeline, backbone of any projects: the data engineers. Literally this happens in both of my jobs. My data engineer colleagues at the consulting firm get involved in all projects. How my second job ended is even more better an example to prove my point: I got redundant after an internal restructuring. After a few months even my boss didn’t survive the changes but the data engineer mate in my team kept his job safe before he quit for another job interesting him more.

Hopefully you find my story interesting and useful for your career consideration. I still strongly believe data scientist is the sexist job of the 21st century and I never stop acquiring knowledge for it. However, before this career fully pans out, getting data engineering skills and experiences helps you secure a job in this industry around data.

Please let me know if my story resonates in you or disagree with my opinions. Any criticism is welcomed. Leave your comments below. Peace.