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Thursday, April 21, 2022

How to Prepare for Google Cloud Data Engineer Exam in 2024?

Hello guys, if you are preparing for Google Cloud Data Engineer exam in 2024 but don't know how to approach this exam so that you can pass in very first attempt then you have come to the right place. Earlier, I have shared many resources for Google Cloud Data Engineer exam like these best Google Cloud Data Engineer courses and Practice tests and today, I am going to share the complete, step-by-step guide to prepare Google Cloud Data Engineer certification. The guide not only list down which topics to prepare, weightage of different topics but also many exam resources like white papers and case studies. In the wake of being affirmed as Google Cloud Professional Architect, I needed to proceed with the force and overcome upon "Google Cloud Certified — Professional Data Engineer" certificate too. It took me approximately 1.5 months (alongside my everyday work) practically to plan for the cert and to feel certain before trying it out.

How to Prepare for Google Cloud Data Engineer Exam?

For those of you acquainted with Google's Cloud Architect exam, as I would see it, the Data Engineer exam questions are somewhat more troublesome however the extension is a whole lot more modest. The Data Engineer affirmation covers a wide scope of subjects including Google Cloud Platform data stockpiling, scientific, AI, and data handling items. 

Here is the thing that everything is covered:


How to Prepare for Google Cloud Data Engineer Exam

1.1 Cloud Storage and Cloud Datastore

Shockingly, these items are not canvassed much in the exam, maybe because they are shrouded all the more broadly in the Cloud Architect exam. Simply know the fundamental ideas of every item and when it is proper (or not fitting) to utilize every item, and you ought to be all around covered.

1.2 Cloud SQL

There were shockingly a couple of inquiries on this item in the exam. Assuming you have viable experience utilizing the item, you ought to be fine to address any inquiries that might come up. Similarly as with questions connected with different data stockpiling items, make certain to know in what situations it is suitable to utilize Cloud SQL and when it would be more proper to utilize Datastore, Bigquery, Bigtable, and so on

1.3 Bigtable

This item is shrouded broadly in the exam. You ought to at minimum know the essential ideas of the item, for example,

step by step instructions to plan a proper composition and column key

Cases, Cluster and Nodes

regardless of whether Bigtable backings exchanges and ACID activities

CBT apparatus

Blueprint plan for time series data

Access control in Bigtable

what as far as possible for Bigtable are (cell and line size, most extreme number of tables, and so on)

1.4 BigQuery

BigQuery will be canvassed in more noteworthy subtleties in exams. Assuming that you know BigQuery, you will actually want to address roughly 40% of inquiries in the exam. You should know about: the fundamental capacities of BigQuery and what sort of issue areas it is reasonable for. BigQuery security and the level at which security can be applied (project and datastore level, however not table or view level)

Parcelled tables, trump card inquiries ("backtick" punctuation)

Sees and their utilization situations

Bringing in/Exporting data to/from BigQuery

have a comprehension of the techniques accessible to associate outer frameworks or devices to BigQuery for examination purposes

how the BigQuery charging model functions, and who gets charged when questions cross venture and charging account limits. Access control in BigQuery, BigQuery best practices, Question plan clarificationHeritage v/s Standard SQL

1.5 Bar/Sub

The exam contains loads of inquiries on this item, however all sensibly significant level so it's only essential to know the fundamental ideas (subjects, memberships, move around conveyance streams, and so on) In particular you should know when it is proper to present Pub/Sub as an informing layer in architecture, for a given arrangement of necessities.

1.6 Apache Hadoop

In fact not a piece of Google Cloud Platform, yet there are a couple of inquiries around this innovation in the exam since it is the basic innovation for Dataproc. Expect a few inquiries on what HDFS, Hive, Pig, Oozie or Sqoop are, yet essential information on what every innovation is and when to utilize it ought to be adequate.

1.7 Cloud Dataflow

Bunches of inquiries on this item, which isn't is business as usual it is a critical area of concentration for Google concerning data handling on Google Cloud Platform. As well as knowing the essential capacities of the item, you will likewise have to comprehend ideas like:

  • Windowing
  • Watermarks and late data
  • Triggers
  • Applying changes
  • collections, and so forth

1.8 Cloud Dataproc

Relatively few inquiries on this other than the Hadoop questions referenced previously. Simply make certain to comprehend the contrasts between Dataproc and Dataflow and when to utilize either. Dataflow is regularly liked for another turn of events, though Dataproc would be required if relocating existing on-premise Hadoop or Spark framework to Google Cloud Platform without redevelopment endeavors.

1.9 TensorFlow, Machine Learning, Cloud DataLab

The exam contains a lot of inquiries on this. You ought to see every one of the fundamental ideas of planning and fostering an AI arrangement on TensorFlow, remembering ideas such as data connection examination for Datalab, and overfitting and how to address it. Definite TensorFlow or Cloud ML programming information isn't needed yet a decent comprehension of AI plan and execution is significant.

1.10 Stackdriver

An astonishing quantity of inquiries on this, considering that Stackdriver is a greater amount of an "operations" item than a "data engineering" item. Make certain to know the sub-results of Stackdriver (Debugger, Error Reporting, Alerting, Trace, Logging), what they do and when they ought to be utilized.

1.11 Data Studio

Relatively few questions on this other than reserving ideas, setting up measurements, aspects and channels in a report.


2. Why get certified as a Google Cloud Professional Data Engineer?

The following are 6 reasons that will make you BADLY need that GCP identification:

  •   GCP Data Engineer is the Highest Paying IT certificate as per a review performed by Global Knowledge
  • You will actually want to configure, assemble and operationalize Data Processing frameworks in cloud/mixture settings. Which is a profoundly esteemed ability on the lookout?
  •   You will further develop your information engineering/framework plan abilities simultaneously.
  • Googlers end up being among individuals who are pushing the limits of Big Data. Huge mechanical forward leaps, for example, MapReduce, BigTable and Dremel formed Data Engineering as far as we might be concerned today. You'll find the opportunity to get familiar with Google's mentality and approach towards DE.
  • You'll get to find out with regards to Machine Learning notwithstanding methods of Product ionizing and Operationalizing AI arrangement on Google Cloud
  •   The vast majority of the abilities are effectively adaptable to other Cloud Providers like AWS and Azure

 3. Is cloud engineering for you?

Assuming that distributed computing sounds applicable to the work you're doing or need to do, then, at that point, cloud designing is an ability you will need to have.

Assuming you're new to Google Cloud Platform (GCP): Google's online seminars on Coursera are an extraordinary learning apparatus and will give you the ability to work in the cloud.

Assuming you as of now cooperate with Google Cloud Platform (GCP): Reading up for and taking the test is a certain method for balancing your range of abilities and acquainting you with components presented by GCP that you maybe knew nothing about.

4. About GCP Data Engineer Certification Exam

There are no prerequisites for Google Cloud Professional Data Engineer certification. 

Duration of the Exam

2 Hours

Exam Fee

USD 200

Exam format

Multiple Choice (Single Answer and Multiple Answers)

Number of Questions


5. How difficult is the Google Cloud Engineer exam?

Since we have examined the configuration and framework of the test, we can come to the principle question – google partner cloud engineer trouble. The Google Cloud Platform Associate Cloud Engineer (ACE) test requires an individual to troubleshoot hypothetical issues in sensible involved situations by considering the information streams. Additionally, Google's ACE test has "Pick the right help" or "What's the best practice" questions.

The test is somewhat troublesome assuming one isn't arranged well. A significant number of the inquiries will challenge one's information just as their time usage abilities. Considering every one of the variables, we can say that it is unquestionably quite a problem. 

In any case, with the right assets and devoted endeavors, you can acquire the affirmation. To assist you with this, we have recorded the assets that you should utilize while planning for the test.

6. Exam Preparation for the Google Cloud Architect Exam: A Step-by-Step Guide

Everybody has an alternate concentrating on method so you should observe one to be that suits your way of life. I'm not recommending that you take on my methodology, however assuming you haven't tracked down your style or have observed that different procedures don't work, go ahead and attempt mine.

6.1 Essential Knowledge

As far as online courses, I suggest the accompanying request.

  • Linux Academy — Big Data Essentials (discretionary) — A non-specialized prologue to the universe of Big Data with a significant level depiction of the Hadoop biological system.
  • Linux Academy — SQL Primer (discretionary) — A prologue to SQL. You will require some fundamental SQL information for BigQuery.
  • Linux Academy — Google Cloud Certified Professional Data Engineer — An inside and out the prologue to the fundamental GCP administrations you can hope to find in the exam.

  • Coursera — Data Engineering with GCP Professional Certificate — Slightly further developed, and zeros in additional on the job of a data engineer in reality.
  • Coursera — Preparing for the Google Cloud Professional Data Engineer Exam — This is the entire of the Coursera course referenced above however I suggest taking this possibly 14 days before the exam. This course covers every one of the places in the authority exam guide.

  • Cloud Academy — Google Data Engineer Exam — Professional Certification Preparation — Good to use as a boost nearer to the exam date. Invest some energy checking out the points that aren't shrouded in other web-based courses.

  • AI Crash Course — Covers the maths and inspiration driving the most regularly involved calculations in data science. For the exam, you should know which calculation is proper for the business necessity including directed (relapse, arrangement), unaided (grouping) and support learning.

6.2 Active practice

Assuming you have relatively little experience utilizing the Google Cloud Platform, I suggest you work on utilizing the active labs and Qwiklabs. You shouldn't remember how to do each assignment, however, use it as a chance to find out about the help and the general GCP climate.

On the off chance that you get the Linux Academy membership, you gain admittance to the cloud sandbox where you can get visitor client accreditations to involve GCP for 3 hours for each meeting.

You can in any case acquire significant practice without breaking your wallet by utilizing this cloud sandbox or by making a free GCP account ($300 free credits) and adhering to the errand directions for the active labs on Coursera or Qwiklabs. (On Qwiklabs, you are basically paying to involve the GCP climate for a decent time frame)

6.3 Past Papers

By concentrating on technique in college was to do the past papers as soon as could really be expected and find out more about the exam style and the kinds of inquiries I can hope to find in the last, most important test. Thusly, you can detect holes in your insight and spotlight your more vulnerable regions.

Assuming you as of now have industry experience, I suggest this methodology. If it's your first time finding out with regards to data engineering, I suggest finishing the Linux Academy course, doing no less than one practices exam, and afterward moving onto the Coursera course.

A month before the genuine exam, I did one practice exam seven days. I utilized the Linux Academy practice exam, the Coursera practice exam, and Google's true Data Engineer practice exam.

The main piece of the training paper step of your amendment is to make a note of the relative multitude of inquiries you got off-base and survey them once more. When you view the responses (in the wake of stepping through the examinations on the internet-based courses), you will see a short clarification of why your response was correct or wrong (and now and again a connection to the video where the point was covered). For Google's true practice exam, they'll likewise give you connections to their authority documentation on the GCP site.

Here are some additional free practice questions: proficient data-engineer/ data-engineer/view/1/

I can't pressure that it is so essential to continue to do rehearse questions. Despite the fact that they don't mirror similar trouble as the genuine exam, you will acquire openness to a wide assortment of inquiries and increment your certainty strolling into the genuine exam.

7. Finishing up Remarks

Nothing is troublesome or unthinkable in the event that it sufficiently endeavors with great planning. Google Cloud Certified Professional Data Engineer is a reasonable certificate for information designing related experts as it allows them an opportunity to stand apart from others. 

To accomplish this accreditation, they need to break a test that requires great planning. The competitors can plan for this test by going through a wide scope of learning assets given by Google Cloud followed by a counterfeit test that can be taken on the Machine Hack stage.

Want to enjoy all that life has to offer to the affirmation competitors!.


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