How I passed the Google Cloud Professional Data Engineer certification exam

Without recommended three years of practical experience.

* Note: the article is dedicated to the Google Cloud Professional Data Engineer certification exam, which was valid until March 29, 2019. After that, some changes occurred - they are described in the " Advanced " section *


Google sweatshirt: yes. Serious face expression: there is. Photo from the video version of this article on YouTube .

Want to get a new sweatshirt, like in my photo?
')
Or maybe you are interested in the certificate of Google Cloud Professional Data Engineer and you are trying to figure out how to get it?

Over the past few months, I took several courses and in parallel worked with Google Cloud - to prepare for the Professional Data Engineer exam. Then I went to the exam and passed it. A few weeks later the hoody arrived - but the certificate came faster.

This article will provide some information that may be useful, and the steps that I have taken to obtain the certificate of Google Cloud Professional Data Engineer.

Transferred to Alconost

Why do I need to get a certificate of Google Cloud Professional Data Engineer?

The data surrounds us, they are everywhere. Therefore, today specialists are in demand who know how to create systems capable of processing and using data. And Google Cloud provides the infrastructure for building these systems.

If you already have the skills to use Google Cloud, how can you demonstrate them to your future employer or client? This can be done in two ways: having a portfolio of projects or having been certified.

The certificate tells potential clients and employers that you have certain skills and that you have made efforts to get their official confirmation.

This is stated in the official description of the exam.

Demonstrate your ability to design and build data processing systems and machine learning models on the Google Cloud platform.

If you do not already have the relevant skills, then when you study training materials for certification, you will learn everything you need about how to create data processing systems of the highest level using Google Cloud.

Who needs to get a Google Cloud Professional Data Engineer certificate?

You saw the numbers - the sphere of cloud technologies is growing, they are with us for a long time. If you are not familiar with the statistics, just believe: the "clouds" are now on the rise.

If you already work as a data processing or analysis specialist, machine learning engineer, or want to move into the data processing industry, then the Google Cloud Professional Data Engineer certification is what you need.

The ability to use cloud technologies becomes a mandatory requirement for all professionals working with data.

Do I need a certificate to be a professional in data processing, data analysis or machine learning?

Not.

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

A certificate is just one way to prove your skills.

How much is it?

The cost of passing the exam is $ 200. If you overwhelm him, you will have to pay again.

In addition, you will have to spend money on preparatory courses and using the platform itself.

The cost of working with the platform is a fee for using Google Cloud services. If you are an active user, you are well aware of this. If you are a beginner and are just starting to study the training materials described in this article, you can create a Google Cloud account and do everything you need, having settled in at $ 300, which Google will credit to your account upon registration.

To the cost of the courses we will pass literally in a moment.

How long is the certificate valid?

Two years. After this period, the exam must pass again.

And since the Google Cloud is constantly evolving, it is likely that the requirements for certification will change (this happened just when I started writing the article).

What do you need to prepare for the exam?

For professional-grade certification, Google recommends having more than three years of industry experience and more than a year in developing and managing solutions using GCP.

I had none of this.

The corresponding experience was about six months in each case.

To fill the gap, I used several online training resources.

What courses did I take?

If your case is similar to mine and you do not meet the recommended requirements, then to increase your own level, you can take some of the following courses.

I used them in preparation for certification. They are listed in order of passage.

For each, I indicated the cost, time and usefulness for the certification exam.


Some of the classroom online learning resources I used to enhance my own skills before the exam are in order: A Cloud Guru , Linux Academy , Coursera .

Data Engineering on Google Cloud Platform Specialization (Cousera)

Cost: $ 49 per month (after a 7-day free trial).
Time: 1-2 months, more than 10 hours a week.
Usefulness: 8 out of 10.

The Course Engineering Data Course on the Google Cloud Platform Specilization on the Coursera platform is developed in collaboration with Google Cloud.

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

If you are not familiar with processing data in Google Cloud, this specialization will give you the necessary skills. You have to complete a series of practical exercises using an iterative platform called QwikLabs. Before that, there will be lectures by experts using Google Cloud on how to use various services, such as Google BigQuery, Cloud Dataproc, Dataflow and Bigtable.

A Cloud Guru Introduction to Google Cloud Platform

Cost: free.
Time: 1 week, 4–6 hours.
Utility: 4 out of 10.

Low utility score does not mean that the course is generally useless - it is not at all. The only reason why the score is so low is that it is not oriented towards the certification of the Professional Data Engineer (as the name implies).

I passed it to refresh my knowledge after passing the Coursera specialization, since I used Google Cloud in some limited cases.

If you’ve previously worked with another cloud provider or have never used Google Cloud, this course may be useful for you: this is a great introduction to the Google Cloud platform as a whole.

Linux Academy Google Certified Professional Data Engineer

Cost: $ 49 per month (after a 7-day free trial).
Time: 1–4 weeks, more than 4 hours per week.
Utility: 10 out of 10.

After passing the exam and reflecting on the courses, I can say that the Linux Academy Google Certified Professional Data Engineer was the most useful.

Video tutorials, as well as the Data Dossier e-book (an excellent free learning resource provided with the course) and training exams make this course one of the best courses I have ever had.

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

Notes in Slak

• Some questions in the exam were not covered either in the Linux Academy course, in A Cloud Guru, or in Google Cloud Practice exams (which was to be expected).
• In one question there was a graph from data points. It was asked how they could be grouped by equation (for example, cos (X) or X² + Y²).
• Be sure to know the differences between Dataflow, Dataproc, Datastore, Bigtable, BigQuery, Pub / Sub and understand how you can use them.
• Two specific examples at the exam are the same as they were at the training ones, although during the exam I did not read them at all (the questions themselves were enough for an answer).
• It is useful to know the basic syntax of SQL queries, especially for questions on BigQuery.
• The Linux Academy and GCP training exams are very similar in style to the questions in the exam — they have to be passed several times to find your own weak points.
• It must be remembered that Dataproc works with Hadoop , Spark , Hive and Pigs .
• Dataflow works with Apache Beam .
• Cloud Spanner is a database originally developed for the cloud, it is compatible with ACID and works anywhere in the world.
• It is useful to know the names of "old men" - equivalents of relational and non-relational databases (for example, MongoDB, Cassandra).
• The IAM roles for services are slightly different, but it would be nice to understand how to share the ability for users to see data and design workflows (for example, you can design workflows in the Dataflow Worker role, but you cannot see data).

So far this is probably enough. Each exam will be held in its own way. The course Linux Academy will give 80% of the required knowledge.

One minute videos about Google Cloud services

Cost: free.
Time: 1-2 hours.
Usefulness: 5 out of 10.

These videos were recommended on the A Cloud Guru forums. Many of them are not related to the certification of the Professional Data Engineer, so I just chose those, the name of the services in which seemed familiar to me.

While completing the course, some services may seem complicated, so it was nice to see how a specific service was described in just 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, more than six hours a week.
Utility: not rated.

I found this resource the day before the scheduled exam date. He did not have enough time to go through - hence the lack of a utility assessment.

However, after reviewing the course overview page, I can say that this is a great resource where you can repeat everything you learned about Data Engineering in Google Cloud and find your weak points.

I talked about this course to one of my colleagues who is preparing for certification.

Google Data Engineering Cheatsheet by Maverick Lin

Cost: free.
Time: unknown.
Utility: not rated.

Another resource I stumbled upon after the exam. It looks comprehensive, but the presentation is rather short. In addition, it is free. It can be accessed between training exams and even after certification - to refresh knowledge.

What did I do after the courses?

Approaching the end of the course, I booked the exam with a weekly notice.

Having a deadline is a great motivation to revise what has been learned.

I passed the Linux Academy and Google Cloud training exams several times until I began to consistently recruit more than 95%.


The first Linux Academy training exam with a score of more than 90%.

The tests for each of the platforms are similar; I wrote down and analyzed the questions I was constantly mistaken about - it helped to eliminate the weak points.

During the actual exam, the theme was the development of data processing systems on Google Cloud with two examples (since March 29, 2019, the content of the exam has changed). The entire exam had multiple choice questions.

The exam took two hours, it seemed to me about 20% more difficult than the familiar to me training exams.

However, the latter is a very valuable resource.

What would I change if I took the exam again?

More training exams. More practical knowledge.

Of course, you can always prepare a little better.

The recommended requirements indicated more than three years of experience in using GCP, which I did not have - so I had to deal with what was.

Additionally

Exam updated March 29. The materials in the article will still provide a good basis for preparation, but it is important to note some changes.

Google Cloud Professional Data Engineer exam sections ( version 1 )

1. Designing data processing systems.
2. Building and maintaining data structures and databases.
3. Data analysis and machine learning connection.
4. Business process modeling for analysis and optimization.
5. Ensuring reliability.
6. Data visualization and decision support.
7. Design with a focus on safety and compliance.

Google Cloud Professional Data Engineer Exam Sections ( Version 2 )

1. Designing data processing systems.
2. Construction and operation of data processing systems.
3. Operation of machine learning models (most changes happened here) [NEW] .
4. Quality assurance solutions.

In version 2, sections 1, 2, 4 and 6 of version 1 are combined into sections 1 and 2, sections 5 and 7 - into section 4. Section 3 in version 2 has been expanded and now covers all the new features of machine learning in Google Cloud.

These changes occurred quite recently, so many training materials did not have time to update.

However, if you use the materials from the article, this should be enough to cover 70% of the required knowledge. I would also independently familiarize myself with the following topics (they appeared in the second version of the exam):

• Google Machine Learning API (ML).
  
• The core of Google Cloud learning.
  
• TPU for Google Cloud (equipment developed by Google specifically for machine learning).
  
• Google glossary with machine learning terms.
  

As you can see, the exam update is primarily related to the machine learning capabilities in the Google Cloud.

Update as of April 29, 2019. I received a message from a Linux Academy teacher (Matthew Ulasien).

Just for reference: we plan to update the Data Engineer course in the Linux Academy and reflect in it new goals - somewhere from the middle or the end of May.

After exam

After passing the exam, you get the result "passed" or "not passed." On training exams, it is advised to strive for at least 70%, so I aimed at 90%.

After successfully passing the exam, you will receive an activation code with an official Google Cloud Professional Data Engineer certificate. Congratulations!

The activation code can be used in the exclusive Google Cloud Professional Data Engineer store, where you can make good money: there are t-shirts, backpacks and sweatshirts (by the time you donate something may not be available). I chose a sweatshirt.

Having received a certificate, you can demonstrate your skills (officially) and return to the work that you do best - building systems.

See you in two years - on re-certification.

P. S. Many thanks to the wonderful teachers of the above courses and Max Kelsen for providing the resources and time to study and prepare for the exam.

About the translator

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