From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job. The course follows the organization of the Exam Guide outline, presenting highest-level concepts, "touchstones", for you to determine whether you feel confident about your knowledge of that area and its dependent concepts, or if you want more study. You also will learn about and have the opportunity to practice key job skills, including cognitive skills such as case analysis, identifying technical watchpoints, and developing proposed solutions. These are job skills that are also exam skills. You will also test your basic abilities with Activity Tracking Challenge Labs. And you will have many sample questions similar to those on the exam, including solutions. The end of the course contains an ungraded practice exam quiz, followed by a graded practice exam quiz that simulates the exam-taking experience.
Introduction
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Welcome to Preparing for the Professional Data Engineer Exam
Description of this module...
Преподаватели
Google Cloud Training
Hi? Welcome to this course,
Preparing for the Professional Data Engineer Exam.
I'm a Technical Curriculum Developer with Google,
my name is Tom Stern.
The tips sections of this course are organized
around a data engineer exam guide outline.
The outline divides the exam into sections,
that focus on specific priorities
and information about the job role.
So this course follows the outline,
explaining each section of the course providing tips
and highlighting information that
would be useful to know.
Some items in the outline are important to
the job of a professional data engineer,
but they're not technical,
or maybe they're not specific to Google.
For example, a data engineer needs to know how to
present solution proposals to customers,
and to communicate with executive staff.
That isn't something we currently teach,
but you should know it from
experience or learn it on your own.
I'll identify these items
when they appear on the outline.
This course isn't about following some pattern exactly,
it's about being useful to help you decide
the best ways for you to prepare for the exam.
I think these three categories
of data representations, pipelines,
and processing infrastructure are
a good way of organizing the information,
and it helps you prepare for
the exam by surfacing what's important.
It's not just a list of details,
or trivial facts that are being tested,
it's the ability to perform the job which means
thinking through data engineering
problems in the abstract,
and using these categories of abstraction.
It's a good way to organize
your thinking about preparing for the exam.
So wrapping up this point,
the general organization of the class
is that it follows the exam guide,
but more specifically we cover
topics where they make the most sense for learning.
Finally, I want to highlight to
you that we're all different,
you have unique experiences, knowledge, and skills.
So there isn't one right way to prepare for the exam.
What's most important in this process is that
you're exposed to many different ways to prepare,
and many different kinds of resources,
and through this exposure you can define
your own unique and
individualized approach to preparation.
A word about ML,
which stands for Machine Learning.
In recent years the industry evolved from
database technologies, to big data,
and data processing technologies,
and now the industry is continuing to
evolve from big data to machine learning.
Big data didn't replace database technology,
Hadoop isn't a replacement for MySQL,
and machine learning and tensor flow does not
replace or eliminate big data or Hadoop in any way.
But what it does is it brings
an entirely new perspective to data engineering.
For the first time,
we can take data that was complicated and maybe
even collected without any particular purpose in mind,
and we can extract business intelligence from it.
Machine Learning enables product innovation,
making products better,
and process innovation making processes more efficient,
and it brings a new kind of
analysis to business decision-making.
So, ML isn't a subject that's tacked
on or just included along with data engineering,
it's a major part of data engineering,
and you'll see it mentioned extensively in this course.
Here's an agenda for this course.
The course begins by discussing
what the certification is about,
how its position relative
to other certifications and more
specifically how it's designed relative to your job role,
experience, and career aspirations.
In the second part of the course,
we will explore the published example case studies.
These case studies are provided
by the certification organization,
and they are cases from which
actual exam questions could be drawn.
So, learning about these case studies
is a good way to prepare.
The next three modules in the course covers
specific preparation tips and technologies.
Some of the information in
these parts are what you might expect,
information on how to choose among related technologies.
For example, under what conditions would
you choose BigTable over BigQuery?
But, there's other information.
One of the things we do in this course is highlight
some sophisticated element of
data engineering on Google Cloud.
This is a way for us to convey many topics very fast.
For example, one slide describes
that subnets extend across zones within a region.
This characteristic is unique to
Google Cloud and different from most other cloud vendors.
The reason for this design is it makes
design for reliability easier since
adjacent instances can be in
the same subnet but on different zones,
giving them fault isolation.
Now that's a pretty sophisticated concept,
but it's important for a data engineer
to know when designing,
processing infrastructure on the Google Cloud.
So in this course, we don't teach you about
regions or zones or subnets,
that information is covered in our other courses.
But what we do is highlight
the skill you need to know for the job,
and then you'll either know
the dependent concepts on which it's based,
or you can go study them to fill in the gaps.
It seems pretty obvious
but you should know that you can't
expect to pass the exam from taking a single course.
You can't learn everything you need in order to be
a competent professional data engineer
in a single course or in a single day.
But what you will get from this class is
a high-level overview of subject areas and tips,
and practice with exam taking skills,
and that'll help you prepare.
If you've already been studying and
preparing for the exam,
this course will help you develop
a good sense of what else you need to study,
or whether you're ready to attempt the exam.
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