How I passed the Google Cloud Professional Data Engineer certification exam

Without recommended 3 years of practical experience

In anticipation of the start of classes in the course “Data Engineer” , we want to share with you a translation of one very interesting story that will surely be useful to future engineers. Go!


Hoodie from Google: put on. Serious working facial expression: present. Photo from the video version of this article on YouTube .

Note. This article focuses on the Google Cloud Professional Data Engineer certification exam until March 29, 2019. After this date there have been some changes. I included them in the "Advanced" section.

So you want to get a new hoodie, like on my cover? Or are you thinking of getting a Google Cloud Professional Data Engineer certificate and wondering how to do this?

In the past few months, I took courses along with using Google Cloud to prepare for the exam for the title of professional data engineer. Then I tried to pass it and passed it. And in a few weeks my hoody was delivered. The certificate came faster.

This article will list a few things you might want to know, and the steps I took to get the Google Cloud Professional Data Engineer certificate.
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Why would you want to be certified by Google Cloud Professional Data Engineer?

Data is everywhere. And knowledge of how to create systems that can process and use data is in demand. Google Cloud provides the infrastructure for building these systems.

You may already have the skills to use Google Cloud, but how will you demonstrate this to a future employer or client? There are two ways: a project portfolio or certification.

The certificate says to future clients and employers: “I have the skills and I made an effort to get accredited.”

A brief description from Google sums it up.

Demonstrate your ability to design and create data processing systems, as well as create machine learning models on the Google Cloud Platform.

If you don’t have the skills yet, getting familiar with training materials for certification means that you’ll learn all about how to create world-class data processing systems on Google Cloud.

Who would want to get certified with Google Cloud Professional Data Engineer?

You saw the numbers. The cloud is growing. It is already here and is not going to go anywhere. If you have not seen the numbers, believe me, the cloud is growing.

If you are already a data specialist, data engineer, data analyst, machine learning engineer, or looking for career opportunities in the data world, you are certified by Google Cloud Professional Data Engineer.

The ability to use cloud technologies is becoming a requirement for any data-oriented position.

Do you need a certificate to be a good data engineer / data security / machine learning engineer?

Not.

You can still use Google Cloud to work with data transfer solutions without a certificate.

A certificate is only one of the methods of confirming existing skills.

How much is it?

The cost of the exam is $ 200. If you fail, you will have to pay again for a new attempt.

Possible costs associated with the preparatory courses and the use of the platform itself.

Platform costs are charges for using Google Cloud services. If you are a sophisticated user, you are already aware of this. If not, and you only get acquainted with the training materials described in this article, you can create a new Google Cloud account and keep within the $ 300 that Google offers during registration.

We will proceed to the course price in a second.

How long is certification current?

2 years. After that you will need to pass the exam again.

And since Google Cloud is evolving every day, it’s likely that what is required for a certificate will change (as I found out, it has already changed by the time I started writing this article).

What do you need to prepare for the exam?

Google recommends more than 3 years of industry experience and more than 1 year of developing and managing solutions using GCP for professional certification.

I had none of the above.

From the strength of 6 months of relevant experience. To compensate for the shortage, I used a combination of online training resources.

What courses did I take?

If you are the same as me and you do not have recommended requirements, you can take some of the following courses to improve your qualifications.

The following courses are what I used to prepare for certification. They are listed in order of completion.

I have indicated the cost, time and usefulness for passing the certification exam for everyone.



Some of the great online resources that I used to upgrade to the exam. In order: Cloud Guru , Linux Academy and Coursera .

Data Engineering on the Google Cloud Platform from Coursera

Cost : $ 49 per month (after a 7-day free trial)
Time : 1-2 months, 10+ hours per week
Usefulness : 8/10

Data Engineering on the Google Cloud Platform from Coursera was created in collaboration with Google Cloud.

It is divided into five subcourses, each of which takes about 10 hours per week of study time.

If you are not familiar with processing data in Google Cloud, this specialization will increase your level from 0 to 1. You will go through a series of practical exercises using an iterative platform called QwikLabs. Prior to this, there will be lectures by Google Cloud practitioners on how to use various services, such as Google BigQuery, Cloud Dataproc, Dataflow and Bigtable.

Cloud Guru introduction to the Google Cloud Platform

Cost : Free
Time : 1 week, 4–6 hours
Usefulness : 4/10

Do not consider the low score of utility for the rate of uselessness of the course. Far from it. The only reason he gets a lower score is that he is not focused on certification of a professional data engineer (this can be understood from the name).

After completing the Coursera specialization, I took this course as retraining, because I used Google Cloud for only a few specialized cases.

If you came from another cloud service provider or have never used Google Cloud before, you probably should have completed this course. This is a great introduction to the Google Cloud Platform as a whole.

Google Certified Professional Linux Academy Data Engineer

Cost : $ 49 per month (after a 7-day free trial)
Time : 1–4 weeks, 4+ hours per week
Usefulness : 10/10

After completing the exam and thinking about the courses I took, the most useful was Google Certified professional Linux Academy data engineer .

The video, as well as the Data Dossier e-book (an excellent free learning resource that came with the course) and practical exams made this course one of the best learning resources I have ever used.

I even recommended it as reference material in some Slack notes for the team after the exam.

Slack Notes

• Some of the things on the exam were not on the exams either in Linux Academy, or in Cloud Guru, or in Google Cloud Practice (expected)
• 1 question with a graph from data points about which equation you should group them (for example, cos (X) or X² + Y²)
• Knowing the differences between Dataflow, Dataproc, Datastore, Bigtable, BigQuery, Pub / Sub and how you can use them is mandatory.
• Two working examples of research on the exam were exactly the same as in the practical classes, although I did not turn to these studies at all during the exam (the questions provided sufficient insight).
• Knowing the basic syntax of SQL queries is very useful, especially for questions about BigQuery.
• The practical exams provided by Linux Academy and GCP are very similar in style to the exam questions, and I will work each one out a few times and use them to figure out your weak points.
• A little admonition to help with Dataproc: “ Dataproc and Dadaproc and Hadoop Elephant are planning to make a fire ( Spark - spark, fire a fire - make a fire) and cook a swarm ( Hive ) of pigs ( Pig )} (Dataproc deals with Hadoop, Spark, Hive and Pig)
• " Dataflow is a Beam of Light is flowing" { Dataflow is the current beam of light} (Dataflow deals with Apache Beam)
• "Everybody around the world could deal with purified acid ( ACID ) with a good wrench (Spanner)} (Cloud Spanner is a database designed to raise a cloud with scratch compatible with ACID and available worldwide)
• Knowing the names of the classical variants of relational and non-relational databases (for example, MongoDB, Cassandra) can be useful.
• IAM roles are slightly different for each service, but it’s helpful to understand how to separate users from seeing data without depriving them of the ability to design workflows (for example, the Dataflow Worker role can design workflows but not see data)

This is probably enough for now. The mileage will probably be different from the exam for the exam. The course Linux Academy will give 80% of knowledge.

1 minute Google Cloud videos

Cost : Free
Time : 1-2 hours
Usefulness : 5/10

They were recommended on the Cloud Guru forums. Many of them were not related to the certification of the Professional Data Engineer, however I selected some of them that are suitable.

Some services may seem complicated when passing the course, so it was nice to hear how a specific service is described in a minute.

Preparing for the Cloud Professional Data Engineer Exam

Cost : $ 49 for a certificate or for free (without a certificate)
Time : 1-2 weeks, 6+ hours per week
Usefulness : N / A

I found this resource the day before the scheduled exam. I did not finish it due to time constraints, hence the lack of a utility rating.

However, judging by the course overview page, it looks like an excellent resource to bring together all that you learned about Google Cloud Data Engineering and highlight any weaknesses.

I advised this course as a resource to one of my colleagues who is preparing for certification.

Cheat Sheet by Google Data Engineering by Meverick Lina

Cost : Free
Time : N / A
Usefulness : N / A

It was another resource I stumbled upon after the exam. In my opinion, it is comprehensive, but at the same time it is concise. Plus, it's free. It can be used to read between practical exams or even after certification to refresh knowledge.

What did I do after the courses?

Coming closer to the end of the course, I booked the exam with a weekly notice.
Having a deadline is a great motivation to reinforce what you have learned.

I repeatedly passed practical exams from Linux Academy and Google Cloud, until I was able to complete them with an accuracy of 95% + each time.


Taking a practical Linux Academy exam for more than 90% for the first time.

The tests from each platform are similar, but I found that, going through the questions that I constantly answered incorrectly, and writing down why I misunderstood them, helped to improve my weak points.

The exam, which I passed, used as a topic two examples of research projects for developing data processing systems on the Google Cloud (this has changed since March 29, 2019). And there was a multiple choice throughout.

He took me about 2 hours. And it was about 20% more difficult than any of the exams that I passed.

I cannot express the value of practical exams sufficiently.

What would I change if I went again?

More practical exams. More practical knowledge.

Of course, there is always more preparation you can do.

Recommended requirements include more than 3 years of GCP usage. But I did not have this, so I had to deal with what I had.

Additionally

The exam was updated on March 29th. The materials presented in this article still provide a good basis, but it is important to note some changes.

Different sections of the Google Cloud Professional Data Engineer exam ( version 1 )

1. Design of data processing systems
2. Creating and maintaining structures and databases.
3. Data analysis and machine learning
4. Business process modeling for analysis and optimization
5. Reliability assurance
6. Data Visualization and Policy Support
7. Design for safety and compliance

Different sections of the Google Cloud Professional Data Engineer Exam ( version 2 )

1. Design of data processing systems
2. Construction and operation of data processing systems
3. Operationalization of machine learning models (most changes happened here) [NEW]
4. Quality Assurance Solutions

Version 2 combined sections 1, 2, 4 and 6 of Version 1 into 1 and 2. It also combined sections 5 and 7 from Version 1 into section 4. And section 3 of Version 2 was expanded to cover all the new Google Cloud machine learning features.

Since these changes occurred quite recently, many study materials did not have the opportunity to be updated.

However, reading the materials of this article should be enough to cover 70% of what you need. I would combine this with some of your own research on the following questions (they were presented in the second version of the exam).

• Google Machine Learning API (ML)
• Google Cloud Machine Learning Engine
• Google Cloud TPUs (user equipment developed by Google specifically for ML training)
• ML Glossary

As you can see, the latest exam update has focused on ML capabilities in the Google Cloud.

Update 04/04/2019 : message from Linux Academy course teacher Matthew Ulasein.
Just for reference, we plan to update the Data Engineer course in the Linux Academy to reflect the new directions that will start somewhere in the middle / end of May.

After exam

When you pass the exam, you only get a successful or negative result. I advise you to strive for at least 70%, so I aimed for at least 90% in practical exams.

After going through, you will receive a redemption code via email along with the official Google Cloud Professional Data Engineer certificate. Congratulations!

You can use the redemption code in the exclusive Google Cloud Professional Data Engineer store, which is crammed with swag ( SWAG ). There are t-shirts, backpacks and hoodies (they may be different from what is in stock, by the time you get there). I chose hoodie.

Now that you are certified, you can demonstrate your skill set (officially) and return to what you do best, to design.

See you in two years to pass the recertification.

PS: If you have any questions or want to clarify something, you can find me on Twitter and LinkedIn . YouTube also has a video version of this article.
PPS: Many thanks to all the wonderful teachers in all the above courses and to Max Kelsen for providing the resources and time to study and prepare for the exam.

And anyone who wants to learn more about the program of the course, the features of the online format, skills, competencies and perspectives that await graduates after training, we invite you to the open day , which will be held today at 20.00.